Hybrid Discriminative-Generative Approach with Gaussian Processes

Ricardo Andrade Pacheco, James Hensman, Max Zwiessele, Neil D. Lawrence
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:47-56, 2014.

Abstract

Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels.

Cite this Paper

BibTeX
@InProceedings{pmlr-v33-andradepacheco14, title = {Hybrid Discriminative-Generative Approach with {G}aussian Processes}, author = {Andrade Pacheco, Ricardo and Hensman, James and Zwiessele, Max and Lawrence, Neil D.}, booktitle = {Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics}, pages = {47--56}, year = {2014}, editor = {Kaski, Samuel and Corander, Jukka}, volume = {33}, series = {Proceedings of Machine Learning Research}, address = {Reykjavik, Iceland}, month = {22--25 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v33/andradepacheco14.pdf}, url = {https://proceedings.mlr.press/v33/andradepacheco14.html}, abstract = {Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels.} }
Endnote
%0 Conference Paper %T Hybrid Discriminative-Generative Approach with Gaussian Processes %A Ricardo Andrade Pacheco %A James Hensman %A Max Zwiessele %A Neil D. Lawrence %B Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2014 %E Samuel Kaski %E Jukka Corander %F pmlr-v33-andradepacheco14 %I PMLR %P 47--56 %U https://proceedings.mlr.press/v33/andradepacheco14.html %V 33 %X Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels.
RIS
TY - CPAPER TI - Hybrid Discriminative-Generative Approach with Gaussian Processes AU - Ricardo Andrade Pacheco AU - James Hensman AU - Max Zwiessele AU - Neil D. Lawrence BT - Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics DA - 2014/04/02 ED - Samuel Kaski ED - Jukka Corander ID - pmlr-v33-andradepacheco14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 33 SP - 47 EP - 56 L1 - http://proceedings.mlr.press/v33/andradepacheco14.pdf UR - https://proceedings.mlr.press/v33/andradepacheco14.html AB - Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels. ER -
APA
Andrade Pacheco, R., Hensman, J., Zwiessele, M. & Lawrence, N.D.. (2014). Hybrid Discriminative-Generative Approach with Gaussian Processes. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 33:47-56 Available from https://proceedings.mlr.press/v33/andradepacheco14.html.

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