2018
|
Gregorová, Magda; Ramapuram, Jason; Kalousis, Alexandros; Marchand-Maillet, Stéphane Large-scale Nonlinear Variable Selection via Kernel Random Features Journal Article CoRR, abs/1804.07169 , 2018. Links | BibTeX @article{DBLP:journals/corr/abs-1804-07169,
title = {Large-scale Nonlinear Variable Selection via Kernel Random Features},
author = {Magda Gregorová and Jason Ramapuram and Alexandros Kalousis and Stéphane Marchand-Maillet},
url = {http://arxiv.org/abs/1804.07169},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1804.07169},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Gregorová, Magda; Kalousis, Alexandros; Marchand-Maillet, Stéphane Structured nonlinear variable selection Journal Article CoRR, abs/1805.06258 , 2018. Links | BibTeX @article{DBLP:journals/corr/abs-1805-06258,
title = {Structured nonlinear variable selection},
author = {Magda Gregorová and Alexandros Kalousis and Stéphane Marchand-Maillet },
url = {http://arxiv.org/abs/1805.06258},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1805.06258},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Blondé, Lionel; Kalousis, Alexandros Sample-Efficient Imitation Learning via Generative Adversarial Nets Journal Article CoRR, abs/1809.02064 , 2018. Links | BibTeX @article{DBLP:journals/corr/abs-1809-02064b,
title = {Sample-Efficient Imitation Learning via Generative Adversarial Nets},
author = {Lionel Blondé and Alexandros Kalousis},
url = {http://arxiv.org/abs/1809.02064},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1809.02064},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Lavda, Frantzeska; Ramapuram, Jason; Gregorova, Magda; Kalousis, Alexandros Continual Classification Learning Using Generative Models Journal Article CoRR, abs/1810.10612 , 2018. Links | BibTeX @article{DBLP:journals/corr/abs-1810-10612,
title = {Continual Classification Learning Using Generative Models},
author = {Frantzeska Lavda and Jason Ramapuram and Magda Gregorova and Alexandros Kalousis},
url = {http://arxiv.org/abs/1810.10612},
year = {2018},
date = {2018-01-01},
journal = {CoRR},
volume = {abs/1810.10612},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2017
|
Mollaysa, Amina; Strasser, Pablo; Kalousis, Alexandros Regularising Non-linear Models Using Feature Side-information Inproceedings Proceedings of the 34th International Conference on Machine Learning,
ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, pp. 2508–2517, 2017. Abstract | Links | BibTeX @inproceedings{DBLP:conf/icml/MollaysaSK17,
title = {Regularising Non-linear Models Using Feature Side-information},
author = {Amina Mollaysa and Pablo Strasser and Alexandros Kalousis},
url = {http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a.pdf
http://proceedings.mlr.press/v70/mollaysa17a/mollaysa17a-supp.pdf
https://bitbucket.org/dmmlgeneva/code_icml2017_regularising-non-linear-models-using-feature},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 34th International Conference on Machine Learning,
ICML 2017, Sydney, NSW, Australia, 6-11 August 2017},
pages = {2508--2517},
crossref = {DBLP:conf/icml/2017},
abstract = {Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. We control the structures of the learned models so that they reflect features’ similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Very often features come with their own vectorial descriptions which provide detailed information about their properties. We refer to these vectorial descriptions as feature side-information. In the standard learning scenario, input is represented as a vector of features and the feature side-information is most often ignored or used only for feature selection prior to model fitting. We believe that feature side-information which carries information about features intrinsic property will help improve model prediction if used in a proper way during learning process. In this paper, we propose a framework that allows for the incorporation of the feature side-information during the learning of very general model families to improve the prediction performance. We control the structures of the learned models so that they reflect features’ similarities as these are defined on the basis of the side-information. We perform experiments on a number of benchmark datasets which show significant predictive performance gains, over a number of baselines, as a result of the exploitation of the side-information. |
Gregorová, Magda; Kalousis, Alexandros; Marchand-Maillet, Stéphane Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks Inproceedings Proceedings of The 9th Asian Conference on Machine Learning, ACML
2017, Seoul, Korea, November 15-17, 2017., pp. 161–176, 2017. Abstract | Links | BibTeX @inproceedings{DBLP:conf/acml/GregorovaKM17,
title = {Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks},
author = {Magda Gregorová and Alexandros Kalousis and Stéphane Marchand-Maillet},
url = {http://proceedings.mlr.press/v77/gregorova17a/gregorova17a.pdf
http://proceedings.mlr.press/v77/gregorova17a/gregorova17a-supp.pdf
https://bitbucket.org/dmmlgeneva/var-leading-indicators},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of The 9th Asian Conference on Machine Learning, ACML
2017, Seoul, Korea, November 15-17, 2017.},
pages = {161--176},
crossref = {DBLP:conf/acml/2017},
abstract = {We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The method is based on the classical linear vector autoregressive model (VAR) and links the discovery of the leading indicators to inferring sparse graphs of Granger causality. We formulate a new constrained optimisation problem to promote the desired sparse structures across the models and the sharing of information amongst the learning tasks in a multi-task manner. We propose an algorithm for solving the problem and document on a battery of synthetic and real-data experiments the advantages of our new method over baseline VAR models as well as the state-of-the-art sparse VAR learning methods. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a new method for forecasting systems of multiple interrelated time series. The method learns the forecast models together with discovering leading indicators from within the system that serve as good predictors improving the forecast accuracy and a cluster structure of the predictive tasks around these. The method is based on the classical linear vector autoregressive model (VAR) and links the discovery of the leading indicators to inferring sparse graphs of Granger causality. We formulate a new constrained optimisation problem to promote the desired sparse structures across the models and the sharing of information amongst the learning tasks in a multi-task manner. We propose an algorithm for solving the problem and document on a battery of synthetic and real-data experiments the advantages of our new method over baseline VAR models as well as the state-of-the-art sparse VAR learning methods. |
Gregorová, Magda; Kalousis, Alexandros; Marchand-Maillet, Stéphane Forecasting and Granger Modelling with Non-linear Dynamical Dependencies Inproceedings Machine Learning and Knowledge Discovery in Databases - European Conference,
ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings,
Part II, pp. 544–558, 2017. Abstract | Links | BibTeX @inproceedings{DBLP:conf/pkdd/GregorovaKM17,
title = {Forecasting and Granger Modelling with Non-linear Dynamical Dependencies},
author = {Magda Gregorová and Alexandros Kalousis and Stéphane Marchand-Maillet},
url = {https://hesso.tind.io/record/2097/files/Gregorova_Kalousis_2017_forecasting_and_granger.pdf
https://bitbucket.org/dmmlgeneva/nonlinear-granger},
doi = {10.1007/978-3-319-71246-8_33},
year = {2017},
date = {2017-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases - European Conference,
ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings,
Part II},
pages = {544--558},
crossref = {DBLP:conf/pkdd/2017-2},
abstract = {Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional linear methods for forecasting multivariate time series are not able to satisfactorily model the non-linear dependencies that may exist in non-Gaussian series. We build on the theory of learning vector-valued functions in the reproducing kernel Hilbert space and develop a method for learning prediction functions that accommodate such non-linearities. The method not only learns the predictive function but also the matrix-valued kernel underlying the function search space directly from the data. Our approach is based on learning multiple matrix-valued kernels, each of those composed of a set of input kernels and a set of output kernels learned in the cone of positive semi-definite matrices. In addition to superior predictive performance in the presence of strong non-linearities, our method also recovers the hidden dynamic relationships between the series and thus is a new alternative to existing graphical Granger techniques.
|
Ramapuram, Jason; Gregorova, Magda; Kalousis, Alexandros Lifelong Generative Modeling Journal Article CoRR, abs/1705.09847 , 2017. Links | BibTeX @article{DBLP:journals/corr/RamapuramGK17,
title = {Lifelong Generative Modeling},
author = {Jason Ramapuram and Magda Gregorova and Alexandros Kalousis},
url = {http://arxiv.org/abs/1705.09847},
year = {2017},
date = {2017-01-01},
journal = {CoRR},
volume = {abs/1705.09847},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2016
|
Nguyen, Phong; Wang, Jun; Kalousis, Alexandros Factorizing LambdaMART for cold start recommendations Journal Article Machine Learning, 104 (2-3), pp. 223–242, 2016. Abstract | Links | BibTeX @article{DBLP:journals/ml/NguyenWK16,
title = {Factorizing LambdaMART for cold start recommendations},
author = {Phong Nguyen and Jun Wang and Alexandros Kalousis},
url = {https://hesso.tind.io/record/1651/files/Kalousis_2016_factorizing_lambdamart.pdf
https://github.com/nguyenfunk/lambdamart-mf
},
doi = {10.1007/s10994-016-5579-3},
year = {2016},
date = {2016-01-01},
journal = {Machine Learning},
volume = {104},
number = {2-3},
pages = {223--242},
abstract = {Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Motivated by the fact that very often the users’ and items’ descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART matrix factorization (LambdaMART-MF), that learns latent representations of users and items using gradient boosted trees. The algorithm factorizes LambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. We regularise the learned latent representations so that they reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. We also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. The experiments show that the factorization of LambdaMart brings significant performance improvements both in the cold start and the matrix completion settings. The incorporation of regularisation seems to have a smaller performance impact.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Motivated by the fact that very often the users’ and items’ descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART matrix factorization (LambdaMART-MF), that learns latent representations of users and items using gradient boosted trees. The algorithm factorizes LambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. We regularise the learned latent representations so that they reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. We also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. The experiments show that the factorization of LambdaMart brings significant performance improvements both in the cold start and the matrix completion settings. The incorporation of regularisation seems to have a smaller performance impact. |
2015
|
Keet, Maria C; Lawrynowicz, Agnieszka; d'Amato, Claudia; Kalousis, Alexandros; Nguyen, Phong; Palma, Raul; Stevens, Robert; Hilario, Melanie The Data Mining OPtimization Ontology Journal Article J. Web Sem., 32 , pp. 43–53, 2015. Abstract | Links | BibTeX @article{DBLP:journals/ws/KeetLdKNPSH15,
title = {The Data Mining OPtimization Ontology},
author = {Maria C Keet and Agnieszka Lawrynowicz and Claudia d'Amato and Alexandros Kalousis and Phong Nguyen and Raul Palma and Robert Stevens and Melanie Hilario},
url = {http://www.dmo-foundry.org/DMOP},
doi = {10.1016/j.websem.2015.01.001},
year = {2015},
date = {2015-01-01},
journal = {J. Web Sem.},
volume = {32},
pages = {43--53},
abstract = {The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of an ontology-based meta-analysis of complete data mining processes in view of extracting patterns associated with mining performance. To this end, DMOP contains detailed descriptions of data mining tasks (e.g., learning, feature selection), data, algorithms, hypotheses such as mined models or patterns, and workflows. A development methodology was used for DMOP, including items such as competency questions and foundational ontology reuse. Several non-trivial modeling problems were encountered and due to the complexity of the data mining details, the ontology requires the use of the OWL 2 DL profile. DMOP was successfully evaluated for semantic meta-mining and used in constructing the Intelligent Discovery Assistant, deployed at the popular data mining environment RapidMiner. |
Sun, Ke; Wang, Jun; Kalousis, Alexandros; Marchand-Maillet, Stéphane Information Geometry and Minimum Description Length Networks Inproceedings Proceedings of the 32nd International Conference on Machine Learning,
ICML 2015, Lille, France, 6-11 July 2015, pp. 49–58, 2015. Abstract | Links | BibTeX @inproceedings{DBLP:conf/icml/SunWKM15,
title = {Information Geometry and Minimum Description Length Networks},
author = {Ke Sun and Jun Wang and Alexandros Kalousis and Stéphane Marchand-Maillet},
url = {http://jmlr.org/proceedings/papers/v37/suna15.html},
year = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning,
ICML 2015, Lille, France, 6-11 July 2015},
pages = {49--58},
crossref = {DBLP:conf/icml/2015},
abstract = {We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We study parametric unsupervised mixture learning. We measure the loss of intrinsic information from the observations to complex mixture models, and then to simple mixture models. We present a geometric picture, where all these representations are regarded as free points in the space of probability distributions. Based on minimum description length, we derive a simple geometric principle to learn all these models together. We present a new learning machine with theories, algorithms, and simulations. |
Sun, Ke; Wang, Jun; Kalousis, Alexandros; Marchand-Maillet, Stéphane Space-Time Local Embeddings Inproceedings Advances in Neural Information Processing Systems 28: Annual Conference
on Neural Information Processing Systems 2015, December 7-12, 2015,
Montreal, Quebec, Canada, pp. 100–108, 2015. Abstract | Links | BibTeX @inproceedings{DBLP:conf/nips/SunWKM15,
title = {Space-Time Local Embeddings},
author = {Ke Sun and Jun Wang and Alexandros Kalousis and Stéphane Marchand-Maillet},
url = {http://papers.nips.cc/paper/5971-space-time-local-embeddings},
year = {2015},
date = {2015-01-01},
booktitle = {Advances in Neural Information Processing Systems 28: Annual Conference
on Neural Information Processing Systems 2015, December 7-12, 2015,
Montreal, Quebec, Canada},
pages = {100--108},
crossref = {DBLP:conf/nips/2015},
abstract = {Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on non-metric datasets show that more information can be preserved in space-time.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on non-metric datasets show that more information can be preserved in space-time. |
2014
|
Wang, Jun; Sun, Ke; Sha, Fei; Marchand-Maillet, Stéphane; Kalousis, Alexandros Two-Stage Metric Learning Inproceedings Proceedings of the 31th International Conference on Machine Learning,
ICML 2014, Beijing, China, 21-26 June 2014, pp. 370–378, 2014. Links | BibTeX @inproceedings{DBLP:conf/icml/WangSSMK14,
title = {Two-Stage Metric Learning},
author = {Jun Wang and Ke Sun and Fei Sha and Stéphane Marchand-Maillet and Alexandros Kalousis},
url = {http://jmlr.org/proceedings/papers/v32/wangc14.html},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 31th International Conference on Machine Learning,
ICML 2014, Beijing, China, 21-26 June 2014},
pages = {370--378},
crossref = {DBLP:conf/icml/2014},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Nguyen, Phong; Hilario, Melanie; Kalousis, Alexandros Using Meta-mining to Support Data Mining Workflow Planning and Optimization Journal Article J. Artif. Intell. Res., 51 , pp. 605–644, 2014. Abstract | Links | BibTeX @article{DBLP:journals/jair/NguyenHK14,
title = {Using Meta-mining to Support Data Mining Workflow Planning and Optimization},
author = {Phong Nguyen and Melanie Hilario and Alexandros Kalousis},
url = {https://doi.org/10.1613/jair.4377},
doi = {10.1613/jair.4377},
year = {2014},
date = {2014-01-01},
journal = {J. Artif. Intell. Res.},
volume = {51},
pages = {605--644},
abstract = {Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Knowledge Discovery in Databases is a complex process that involves many different data processing and learning operators. Today's Knowledge Discovery Support Systems can contain several hundred operators. A major challenge is to assist the user in designing workflows which are not only valid but also -- ideally -- optimize some performance measure associated with the user goal. In this paper we present such a system. The system relies on a meta-mining module which analyses past data mining experiments and extracts meta-mining models which associate dataset characteristics with workflow descriptors in view of workflow performance optimization. The meta-mining model is used within a data mining workflow planner, to guide the planner during the workflow planning. We learn the meta-mining models using a similarity learning approach, and extract the workflow descriptors by mining the workflows for generalized relational patterns accounting also for domain knowledge provided by a data mining ontology. We evaluate the quality of the data mining workflows that the system produces on a collection of real world datasets coming from biology and show that it produces workflows that are significantly better than alternative methods that can only do workflow selection and not planning. |
2013
|
Do, Huyen; Kalousis, Alexandros Convex formulations of radius-margin based Support Vector Machines Inproceedings Proceedings of the 30th International Conference on Machine Learning,
ICML 2013, Atlanta, GA, USA, 16-21 June 2013, pp. 169–177, 2013. Links | BibTeX @inproceedings{DBLP:conf/icml/DoK13,
title = {Convex formulations of radius-margin based Support Vector Machines},
author = {Huyen Do and Alexandros Kalousis},
url = {http://jmlr.org/proceedings/papers/v28/do13.html},
year = {2013},
date = {2013-01-01},
booktitle = {Proceedings of the 30th International Conference on Machine Learning,
ICML 2013, Atlanta, GA, USA, 16-21 June 2013},
pages = {169--177},
crossref = {DBLP:conf/icml/2013},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2012
|
Nguyen, Phong; Wang, Jun; Hilario, Melanie; Kalousis, Alexandros Learning Heterogeneous Similarity Measures for Hybrid-Recommendations
in Meta-Mining Inproceedings 12th IEEE International Conference on Data Mining, ICDM 2012,
Brussels, Belgium, December 10-13, 2012, pp. 1026–1031, 2012. Links | BibTeX @inproceedings{DBLP:conf/icdm/NguyenWHK12,
title = {Learning Heterogeneous Similarity Measures for Hybrid-Recommendations
in Meta-Mining},
author = {Phong Nguyen and Jun Wang and Melanie Hilario and Alexandros Kalousis},
url = {https://doi.org/10.1109/ICDM.2012.41},
doi = {10.1109/ICDM.2012.41},
year = {2012},
date = {2012-01-01},
booktitle = {12th IEEE International Conference on Data Mining, ICDM 2012,
Brussels, Belgium, December 10-13, 2012},
pages = {1026--1031},
crossref = {DBLP:conf/icdm/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Nguyen, Phong; Kalousis, Alexandros Model mining for robust feature selection Inproceedings The 18th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD '12, Beijing, China, August 12-16, 2012, pp. 913–921, 2012. Links | BibTeX @inproceedings{DBLP:conf/kdd/WoznicaNK12,
title = {Model mining for robust feature selection},
author = {Adam Woznica and Phong Nguyen and Alexandros Kalousis},
url = {http://doi.acm.org/10.1145/2339530.2339674},
doi = {10.1145/2339530.2339674},
year = {2012},
date = {2012-01-01},
booktitle = {The 18th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD '12, Beijing, China, August 12-16, 2012},
pages = {913--921},
crossref = {DBLP:conf/kdd/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Wang, Jun; Kalousis, Alexandros; Woznica, Adam Parametric Local Metric Learning for Nearest Neighbor Classification Inproceedings Advances in Neural Information Processing Systems 25: 26th Annual
Conference on Neural Information Processing Systems 2012. Proceedings
of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States., pp. 1610–1618, 2012. Links | BibTeX @inproceedings{DBLP:conf/nips/WangKW12,
title = {Parametric Local Metric Learning for Nearest Neighbor Classification},
author = {Jun Wang and Alexandros Kalousis and Adam Woznica},
url = {http://papers.nips.cc/paper/4818-parametric-local-metric-learning-for-nearest-neighbor-classification},
year = {2012},
date = {2012-01-01},
booktitle = {Advances in Neural Information Processing Systems 25: 26th Annual
Conference on Neural Information Processing Systems 2012. Proceedings
of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States.},
pages = {1610--1618},
crossref = {DBLP:conf/nips/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Wang, Jun; Woznica, Adam; Kalousis, Alexandros Learning Neighborhoods for Metric Learning Inproceedings Machine Learning and Knowledge Discovery in Databases - European Conference,
ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings,
Part I, pp. 223–236, 2012. Links | BibTeX @inproceedings{DBLP:conf/pkdd/WangWK12,
title = {Learning Neighborhoods for Metric Learning},
author = {Jun Wang and Adam Woznica and Alexandros Kalousis},
url = {https://doi.org/10.1007/978-3-642-33460-3_20},
doi = {10.1007/978-3-642-33460-3_20},
year = {2012},
date = {2012-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases - European Conference,
ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings,
Part I},
pages = {223--236},
crossref = {DBLP:conf/pkdd/2012-1},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Do, Huyen; Kalousis, Alexandros; Wang, Jun; Woznica, Adam A metric learning perspective of SVM: on the relation of LMNN
and SVM Inproceedings Proceedings of the Fifteenth International Conference on Artificial
Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands,
April 21-23, 2012, pp. 308–317, 2012. Links | BibTeX @inproceedings{DBLP:journals/jmlr/DoKWW12,
title = {A metric learning perspective of SVM: on the relation of LMNN
and SVM},
author = {Huyen Do and Alexandros Kalousis and Jun Wang and Adam Woznica},
url = {http://jmlr.csail.mit.edu/proceedings/papers/v22/do12.html},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial
Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands,
April 21-23, 2012},
pages = {308--317},
crossref = {DBLP:conf/aistats/2012},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Stendardo, Nabil; Kalousis, Alexandros Relationship-aware sequential pattern mining Journal Article CoRR, abs/1212.5389 , 2012. Links | BibTeX @article{DBLP:journals/corr/abs-1212-5389,
title = {Relationship-aware sequential pattern mining},
author = {Nabil Stendardo and Alexandros Kalousis},
url = {http://arxiv.org/abs/1212.5389},
year = {2012},
date = {2012-01-01},
journal = {CoRR},
volume = {abs/1212.5389},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2011
|
Wang, Jun; Do, Huyen; Woznica, Adam; Kalousis, Alexandros Metric Learning with Multiple Kernels Inproceedings Advances in Neural Information Processing Systems 24: 25th Annual
Conference on Neural Information Processing Systems 2011. Proceedings
of a meeting held 12-14 December 2011, Granada, Spain., pp. 1170–1178, 2011. Links | BibTeX @inproceedings{DBLP:conf/nips/WangDWK11,
title = {Metric Learning with Multiple Kernels},
author = {Jun Wang and Huyen Do and Adam Woznica and Alexandros Kalousis},
url = {http://papers.nips.cc/paper/4399-metric-learning-with-multiple-kernels},
year = {2011},
date = {2011-01-01},
booktitle = {Advances in Neural Information Processing Systems 24: 25th Annual
Conference on Neural Information Processing Systems 2011. Proceedings
of a meeting held 12-14 December 2011, Granada, Spain.},
pages = {1170--1178},
crossref = {DBLP:conf/nips/2011},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Hilario, Melanie; Nguyen, Phong; Do, Huyen; Woznica, Adam; Kalousis, Alexandros Ontology-Based Meta-Mining of Knowledge Discovery Workflows Incollection Meta-Learning in Computational Intelligence, pp. 273–315, 2011. Links | BibTeX @incollection{DBLP:series/sci/HilarioNDWK11,
title = {Ontology-Based Meta-Mining of Knowledge Discovery Workflows},
author = {Melanie Hilario and Phong Nguyen and Huyen Do and Adam Woznica and Alexandros Kalousis},
url = {https://doi.org/10.1007/978-3-642-20980-2_9},
doi = {10.1007/978-3-642-20980-2_9},
year = {2011},
date = {2011-01-01},
booktitle = {Meta-Learning in Computational Intelligence},
pages = {273--315},
crossref = {DBLP:series/sci/2011-358},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}
|
2010
|
Dakna, Mohammed; Harris, Keith; Kalousis, Alexandros; Carpentier, Sebastien; Kolch, Walter; Schanstra, Joost P; Haubitz, Marion; Vlahou, Antonia; Mischak, Harald; Girolami, Mark A Addressing the Challenge of Defining Valid Proteomic Biomarkers and
Classifiers Journal Article BMC Bioinformatics, 11 , pp. 594, 2010. Links | BibTeX @article{DBLP:journals/bmcbi/DaknaHKCKSHVMG10,
title = {Addressing the Challenge of Defining Valid Proteomic Biomarkers and
Classifiers},
author = {Mohammed Dakna and Keith Harris and Alexandros Kalousis and Sebastien Carpentier and Walter Kolch and Joost P Schanstra and Marion Haubitz and Antonia Vlahou and Harald Mischak and Mark A Girolami},
url = {https://doi.org/10.1186/1471-2105-11-594},
doi = {10.1186/1471-2105-11-594},
year = {2010},
date = {2010-01-01},
journal = {BMC Bioinformatics},
volume = {11},
pages = {594},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Woznica, Adam; Kalousis, Alexandros Adaptive Distances on Sets of Vectors Inproceedings ICDM 2010, The 10th IEEE International Conference on Data Mining,
Sydney, Australia, 14-17 December 2010, pp. 579–588, 2010. Links | BibTeX @inproceedings{DBLP:conf/icdm/WoznicaK10,
title = {Adaptive Distances on Sets of Vectors},
author = {Adam Woznica and Alexandros Kalousis},
url = {https://doi.org/10.1109/ICDM.2010.45},
doi = {10.1109/ICDM.2010.45},
year = {2010},
date = {2010-01-01},
booktitle = {ICDM 2010, The 10th IEEE International Conference on Data Mining,
Sydney, Australia, 14-17 December 2010},
pages = {579--588},
crossref = {DBLP:conf/icdm/2010},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Kalousis, Alexandros; Hilario, Melanie Adaptive Matching Based Kernels for Labelled Graphs Inproceedings Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia
Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings.
Part II, pp. 374–385, 2010. Links | BibTeX @inproceedings{DBLP:conf/pakdd/WoznicaKH10,
title = {Adaptive Matching Based Kernels for Labelled Graphs},
author = {Adam Woznica and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1007/978-3-642-13672-6_37},
doi = {10.1007/978-3-642-13672-6_37},
year = {2010},
date = {2010-01-01},
booktitle = {Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia
Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings.
Part II},
pages = {374--385},
crossref = {DBLP:conf/pakdd/2010-2},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Kalousis, Alexandros A New Framework for Dissimilarity and Similarity Learning Inproceedings Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia
Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings.
Part II, pp. 386–397, 2010. Links | BibTeX @inproceedings{DBLP:conf/pakdd/WoznicaK10,
title = {A New Framework for Dissimilarity and Similarity Learning},
author = {Adam Woznica and Alexandros Kalousis},
url = {https://doi.org/10.1007/978-3-642-13672-6_38},
doi = {10.1007/978-3-642-13672-6_38},
year = {2010},
date = {2010-01-01},
booktitle = {Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia
Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings.
Part II},
pages = {386--397},
crossref = {DBLP:conf/pakdd/2010-2},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2009
|
Anagnostopoulos, Theodoros; Anagnostopoulos, Christos; Hadjiefthymiades, Stathes; Kyriakakos, Miltos; Kalousis, Alexandros Predicting the location of mobile users: a machine learning approach Inproceedings Proceedings of the 2009 international conference on Pervasive services,
ICPS '09, London, United Kingdom, July 13-17, 2009, pp. 65–72, 2009. Links | BibTeX @inproceedings{DBLP:conf/icps/Anagnostopoulos09,
title = {Predicting the location of mobile users: a machine learning approach},
author = {Theodoros Anagnostopoulos and Christos Anagnostopoulos and Stathes Hadjiefthymiades and Miltos Kyriakakos and Alexandros Kalousis},
url = {http://doi.acm.org/10.1145/1568199.1568210},
doi = {10.1145/1568199.1568210},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings of the 2009 international conference on Pervasive services,
ICPS '09, London, United Kingdom, July 13-17, 2009},
pages = {65--72},
crossref = {DBLP:conf/icps/2009},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Do, Huyen; Kalousis, Alexandros; Hilario, Melanie Feature Weighting Using Margin and Radius Based Error Bound Optimization
in SVMs Inproceedings Machine Learning and Knowledge Discovery in Databases, European Conference,
ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings,
Part I, pp. 315–329, 2009. Links | BibTeX @inproceedings{DBLP:conf/pkdd/DoKH09,
title = {Feature Weighting Using Margin and Radius Based Error Bound Optimization
in SVMs},
author = {Huyen Do and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1007/978-3-642-04180-8_38},
doi = {10.1007/978-3-642-04180-8_38},
year = {2009},
date = {2009-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases, European Conference,
ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings,
Part I},
pages = {315--329},
crossref = {DBLP:conf/pkdd/2009-1},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Do, Huyen; Kalousis, Alexandros; Woznica, Adam; Hilario, Melanie Margin and Radius Based Multiple Kernel Learning Inproceedings Machine Learning and Knowledge Discovery in Databases, European Conference,
ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings,
Part I, pp. 330–343, 2009. Links | BibTeX @inproceedings{DBLP:conf/pkdd/DoKWH09,
title = {Margin and Radius Based Multiple Kernel Learning},
author = {Huyen Do and Alexandros Kalousis and Adam Woznica and Melanie Hilario},
url = {https://doi.org/10.1007/978-3-642-04180-8_39},
doi = {10.1007/978-3-642-04180-8_39},
year = {2009},
date = {2009-01-01},
booktitle = {Machine Learning and Knowledge Discovery in Databases, European Conference,
ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings,
Part I},
pages = {330--343},
crossref = {DBLP:conf/pkdd/2009-1},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2008
|
Hilario, Melanie; Kalousis, Alexandros Approaches to dimensionality reduction in proteomic biomarker studies Journal Article Briefings in Bioinformatics, 9 (2), pp. 102–118, 2008. Links | BibTeX @article{DBLP:journals/bib/HilarioK08,
title = {Approaches to dimensionality reduction in proteomic biomarker studies},
author = {Melanie Hilario and Alexandros Kalousis},
url = {https://doi.org/10.1093/bib/bbn005},
doi = {10.1093/bib/bbn005},
year = {2008},
date = {2008-01-01},
journal = {Briefings in Bioinformatics},
volume = {9},
number = {2},
pages = {102--118},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Prados, Julien; Kalousis, Alexandros; Hilario, Melanie Feature Selection with the logRatio Kernel Inproceedings Proceedings of the SIAM International Conference on Data Mining,
SDM 2008, April 24-26, 2008, Atlanta, Georgia, USA, pp. 177–187, 2008. Links | BibTeX @inproceedings{DBLP:conf/sdm/PradosKH08,
title = {Feature Selection with the logRatio Kernel},
author = {Julien Prados and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1137/1.9781611972788.16},
doi = {10.1137/1.9781611972788.16},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the SIAM International Conference on Data Mining,
SDM 2008, April 24-26, 2008, Atlanta, Georgia, USA},
pages = {177--187},
crossref = {DBLP:conf/sdm/2008},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Ntoutsi, Irene; Kalousis, Alexandros; Theodoridis, Yannis A general framework for estimating similarity of datasets and decision
trees: exploring semantic similarity of decision trees Inproceedings Proceedings of the SIAM International Conference on Data Mining,
SDM 2008, April 24-26, 2008, Atlanta, Georgia, USA, pp. 810–821, 2008. Links | BibTeX @inproceedings{DBLP:conf/sdm/NtoutsiKT08,
title = {A general framework for estimating similarity of datasets and decision
trees: exploring semantic similarity of decision trees},
author = {Irene Ntoutsi and Alexandros Kalousis and Yannis Theodoridis},
url = {https://doi.org/10.1137/1.9781611972788.73},
doi = {10.1137/1.9781611972788.73},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the SIAM International Conference on Data Mining,
SDM 2008, April 24-26, 2008, Atlanta, Georgia, USA},
pages = {810--821},
crossref = {DBLP:conf/sdm/2008},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2007
|
Kalousis, Alexandros; Prados, Julien; Hilario, Melanie Stability of feature selection algorithms: a study on high-dimensional
spaces Journal Article Knowl. Inf. Syst., 12 (1), pp. 95–116, 2007. Links | BibTeX @article{DBLP:journals/kais/KalousisPH07,
title = {Stability of feature selection algorithms: a study on high-dimensional
spaces},
author = {Alexandros Kalousis and Julien Prados and Melanie Hilario},
url = {https://doi.org/10.1007/s10115-006-0040-8},
doi = {10.1007/s10115-006-0040-8},
year = {2007},
date = {2007-01-01},
journal = {Knowl. Inf. Syst.},
volume = {12},
number = {1},
pages = {95--116},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Woznica, Adam; Kalousis, Alexandros; Hilario, Melanie Learning to combine distances for complex representations Inproceedings Machine Learning, Proceedings of the Twenty-Fourth International Conference
(ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007, pp. 1031–1038, 2007. Links | BibTeX @inproceedings{DBLP:conf/icml/WoznicaKH07,
title = {Learning to combine distances for complex representations},
author = {Adam Woznica and Alexandros Kalousis and Melanie Hilario},
url = {http://doi.acm.org/10.1145/1273496.1273626},
doi = {10.1145/1273496.1273626},
year = {2007},
date = {2007-01-01},
booktitle = {Machine Learning, Proceedings of the Twenty-Fourth International Conference
(ICML 2007), Corvallis, Oregon, USA, June 20-24, 2007},
pages = {1031--1038},
crossref = {DBLP:conf/icml/2007},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Anagnostopoulos, Theodoros; Anagnostopoulos, Christos; Hadjiefthymiades, Stathes; Kalousis, Alexandros; Kyriakakos, Miltiadis Path Prediction through Data Mining Inproceedings Proceedings of the IEEE International Conference on Pervasive Services,
ICPS 2007, 15-20 July, 2007, Istanbul, Turkey, pp. 128–135, 2007. Links | BibTeX @inproceedings{DBLP:conf/icps/AnagnostopoulosAHKK07,
title = {Path Prediction through Data Mining},
author = {Theodoros Anagnostopoulos and Christos Anagnostopoulos and Stathes Hadjiefthymiades and Alexandros Kalousis and Miltiadis Kyriakakos},
url = {https://doi.org/10.1109/PERSER.2007.4283902},
doi = {10.1109/PERSER.2007.4283902},
year = {2007},
date = {2007-01-01},
booktitle = {Proceedings of the IEEE International Conference on Pervasive Services,
ICPS 2007, 15-20 July, 2007, Istanbul, Turkey},
pages = {128--135},
crossref = {DBLP:conf/icps/2007},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2006
|
Prados, Julien; Kalousis, Alexandros; Hilario, Melanie On Preprocessing of SELDI-MS Data and its Evaluation Inproceedings 19th IEEE International Symposium on Computer-Based Medical Systems
(CBMS 2006), 22-23 June 2006, Salt Lake City, Utah, USA, pp. 953–958, 2006. Links | BibTeX @inproceedings{DBLP:conf/cbms/PradosKH06,
title = {On Preprocessing of SELDI-MS Data and its Evaluation},
author = {Julien Prados and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1109/CBMS.2006.122},
doi = {10.1109/CBMS.2006.122},
year = {2006},
date = {2006-01-01},
booktitle = {19th IEEE International Symposium on Computer-Based Medical Systems
(CBMS 2006), 22-23 June 2006, Salt Lake City, Utah, USA},
pages = {953--958},
crossref = {DBLP:conf/cbms/2006},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Kalousis, Alexandros; Hilario, Melanie Distances and (Indefinite) Kernels for Sets of Objects Inproceedings Proceedings of the 6th IEEE International Conference on Data Mining
(ICDM 2006), 18-22 December 2006, Hong Kong, China, pp. 1151–1156, 2006. Links | BibTeX @inproceedings{DBLP:conf/icdm/WoznicaKH06,
title = {Distances and (Indefinite) Kernels for Sets of Objects},
author = {Adam Woznica and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1109/ICDM.2006.60},
doi = {10.1109/ICDM.2006.60},
year = {2006},
date = {2006-01-01},
booktitle = {Proceedings of the 6th IEEE International Conference on Data Mining
(ICDM 2006), 18-22 December 2006, Hong Kong, China},
pages = {1151--1156},
crossref = {DBLP:conf/icdm/2006},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Kalousis, Alexandros; Hilario, Melanie Kernels on Lists and Sets over Relational Algebra: An Application
to Classification of Protein Fingerprints Inproceedings Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia
Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings, pp. 546–551, 2006. Links | BibTeX @inproceedings{DBLP:conf/pakdd/WoznicaKH06,
title = {Kernels on Lists and Sets over Relational Algebra: An Application
to Classification of Protein Fingerprints},
author = {Adam Woznica and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1007/11731139_64},
doi = {10.1007/11731139_64},
year = {2006},
date = {2006-01-01},
booktitle = {Advances in Knowledge Discovery and Data Mining, 10th Pacific-Asia
Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings},
pages = {546--551},
crossref = {DBLP:conf/pakdd/2006},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2005
|
Kalousis, Alexandros; Prados, Julien; Hilario, Melanie Stability of Feature Selection Algorithms Inproceedings Proceedings of the 5th IEEE International Conference on Data Mining
(ICDM 2005), 27-30 November 2005, Houston, Texas, USA, pp. 218–225, 2005. Links | BibTeX @inproceedings{DBLP:conf/icdm/KalousisPH05,
title = {Stability of Feature Selection Algorithms},
author = {Alexandros Kalousis and Julien Prados and Melanie Hilario},
url = {https://doi.org/10.1109/ICDM.2005.135},
doi = {10.1109/ICDM.2005.135},
year = {2005},
date = {2005-01-01},
booktitle = {Proceedings of the 5th IEEE International Conference on Data Mining
(ICDM 2005), 27-30 November 2005, Houston, Texas, USA},
pages = {218--225},
crossref = {DBLP:conf/icdm/2005},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Woznica, Adam; Kalousis, Alexandros; Hilario, Melanie Kernels over Relational Algebra Structures Inproceedings Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia
Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings, pp. 588–598, 2005. Links | BibTeX @inproceedings{DBLP:conf/pakdd/WoznicaKH05,
title = {Kernels over Relational Algebra Structures},
author = {Adam Woznica and Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1007/11430919_68},
doi = {10.1007/11430919_68},
year = {2005},
date = {2005-01-01},
booktitle = {Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia
Conference, PAKDD 2005, Hanoi, Vietnam, May 18-20, 2005, Proceedings},
pages = {588--598},
crossref = {DBLP:conf/pakdd/2005},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Kalousis, Alexandros; Prados, Julien; Rexhepaj, Elton; Hilario, Melanie Feature Extraction from Mass Spectra for Classification of Pathological
States Inproceedings Knowledge Discovery in Databases: PKDD 2005, 9th European Conference
on Principles and Practice of Knowledge Discovery in Databases, Porto,
Portugal, October 3-7, 2005, Proceedings, pp. 536–543, 2005. Links | BibTeX @inproceedings{DBLP:conf/pkdd/KalousisPRH05,
title = {Feature Extraction from Mass Spectra for Classification of Pathological
States},
author = {Alexandros Kalousis and Julien Prados and Elton Rexhepaj and Melanie Hilario},
url = {https://doi.org/10.1007/11564126_55},
doi = {10.1007/11564126_55},
year = {2005},
date = {2005-01-01},
booktitle = {Knowledge Discovery in Databases: PKDD 2005, 9th European Conference
on Principles and Practice of Knowledge Discovery in Databases, Porto,
Portugal, October 3-7, 2005, Proceedings},
pages = {536--543},
crossref = {DBLP:conf/pkdd/2005},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2004
|
Kalousis, Alexandros; ã, Jo; Hilario, Melanie On Data and Algorithms: Understanding Inductive Performance Journal Article Machine Learning, 54 (3), pp. 275–312, 2004. Links | BibTeX @article{DBLP:journals/ml/KalousisGH04,
title = {On Data and Algorithms: Understanding Inductive Performance},
author = {Alexandros Kalousis and Jo ã and Melanie Hilario},
url = {https://doi.org/10.1023/B:MACH.0000015882.38031.85},
doi = {10.1023/B:MACH.0000015882.38031.85},
year = {2004},
date = {2004-01-01},
journal = {Machine Learning},
volume = {54},
number = {3},
pages = {275--312},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Kalousis, Alexandros; Prados, Julien; -, Jean; Allard, Laure; Hilario, Melanie Distilling Classification Models from Cross Validation Runs: An Application
to Mass Spectrometry Inproceedings 16th IEEE International Conference on Tools with Artificial Intelligence
(ICTAI 2004), 15-17 November 2004, Boca Raton, FL, USA, pp. 113–119, 2004. Links | BibTeX @inproceedings{DBLP:conf/ictai/KalousisPSAH04,
title = {Distilling Classification Models from Cross Validation Runs: An Application
to Mass Spectrometry},
author = {Alexandros Kalousis and Julien Prados and Jean - and Laure Allard and Melanie Hilario},
url = {https://doi.org/10.1109/ICTAI.2004.51},
doi = {10.1109/ICTAI.2004.51},
year = {2004},
date = {2004-01-01},
booktitle = {16th IEEE International Conference on Tools with Artificial Intelligence
(ICTAI 2004), 15-17 November 2004, Boca Raton, FL, USA},
pages = {113--119},
crossref = {DBLP:conf/ictai/2004},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2003
|
Kalousis, Alexandros; Hilario, Melanie Representational Issues in Meta-Learning Inproceedings Machine Learning, Proceedings of the Twentieth International Conference
(ICML 2003), August 21-24, 2003, Washington, DC, USA, pp. 313–320, 2003. Links | BibTeX @inproceedings{DBLP:conf/icml/KalousisH03,
title = {Representational Issues in Meta-Learning},
author = {Alexandros Kalousis and Melanie Hilario},
url = {http://www.aaai.org/Library/ICML/2003/icml03-043.php},
year = {2003},
date = {2003-01-01},
booktitle = {Machine Learning, Proceedings of the Twentieth International Conference
(ICML 2003), August 21-24, 2003, Washington, DC, USA},
pages = {313--320},
crossref = {DBLP:conf/icml/2003},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2001
|
Kalousis, Alexandros; Hilario, Melanie Model Selection via Meta-Learning: A Comparative Study Journal Article International Journal on Artificial Intelligence Tools, 10 (4), pp. 525–554, 2001. Links | BibTeX @article{DBLP:journals/ijait/KalousisH01,
title = {Model Selection via Meta-Learning: A Comparative Study},
author = {Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1142/S0218213001000647},
doi = {10.1142/S0218213001000647},
year = {2001},
date = {2001-01-01},
journal = {International Journal on Artificial Intelligence Tools},
volume = {10},
number = {4},
pages = {525--554},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
Bensusan, Hilan; Kalousis, Alexandros Estimating the Predictive Accuracy of a Classifier Inproceedings Machine Learning: EMCL 2001, 12th European Conference on Machine
Learning, Freiburg, Germany, September 5-7, 2001, Proceedings, pp. 25–36, 2001. Links | BibTeX @inproceedings{DBLP:conf/ecml/BensusanK01,
title = {Estimating the Predictive Accuracy of a Classifier},
author = {Hilan Bensusan and Alexandros Kalousis},
url = {https://doi.org/10.1007/3-540-44795-4_3},
doi = {10.1007/3-540-44795-4_3},
year = {2001},
date = {2001-01-01},
booktitle = {Machine Learning: EMCL 2001, 12th European Conference on Machine
Learning, Freiburg, Germany, September 5-7, 2001, Proceedings},
pages = {25--36},
crossref = {DBLP:conf/ecml/2001},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Kalousis, Alexandros; Hilario, Melanie Feature Selection for Meta-learning Inproceedings Knowledge Discovery and Data Mining - PAKDD 2001, 5th Pacific-Asia
Conference, Hong Kong, China, April 16-18, 2001, Proceedings, pp. 222–233, 2001. Links | BibTeX @inproceedings{DBLP:conf/pakdd/KalousisH01,
title = {Feature Selection for Meta-learning},
author = {Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1007/3-540-45357-1_26},
doi = {10.1007/3-540-45357-1_26},
year = {2001},
date = {2001-01-01},
booktitle = {Knowledge Discovery and Data Mining - PAKDD 2001, 5th Pacific-Asia
Conference, Hong Kong, China, April 16-18, 2001, Proceedings},
pages = {222--233},
crossref = {DBLP:conf/pakdd/2001},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
Hilario, Melanie; Kalousis, Alexandros Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection Inproceedings Principles of Data Mining and Knowledge Discovery, 5th European Conference,
PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings, pp. 180–191, 2001. Links | BibTeX @inproceedings{DBLP:conf/pkdd/HilarioK01,
title = {Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection},
author = {Melanie Hilario and Alexandros Kalousis},
url = {https://doi.org/10.1007/3-540-44794-6_15},
doi = {10.1007/3-540-44794-6_15},
year = {2001},
date = {2001-01-01},
booktitle = {Principles of Data Mining and Knowledge Discovery, 5th European Conference,
PKDD 2001, Freiburg, Germany, September 3-5, 2001, Proceedings},
pages = {180--191},
crossref = {DBLP:conf/pkdd/2001},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
2000
|
Kalousis, Alexandros; Hilario, Melanie Model selection via meta-learning: a comparative study Inproceedings 12th IEEE International Conference on Tools with Artificial Intelligence
(ICTAI 2000), 13-15 November 2000, Vancouver, BC, Canada, pp. 406–413, 2000. Links | BibTeX @inproceedings{DBLP:conf/ictai/KalousisH00,
title = {Model selection via meta-learning: a comparative study},
author = {Alexandros Kalousis and Melanie Hilario},
url = {https://doi.org/10.1109/TAI.2000.889901},
doi = {10.1109/TAI.2000.889901},
year = {2000},
date = {2000-01-01},
booktitle = {12th IEEE International Conference on Tools with Artificial Intelligence
(ICTAI 2000), 13-15 November 2000, Vancouver, BC, Canada},
pages = {406--413},
crossref = {DBLP:conf/ictai/2000},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
