Hybrid Discriminative-Generative Approaches with Gaussian Processes

Ricardo Andrade-Pacheco; James Hensman; Neil D. Lawrence

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Hybrid Discriminative-Generative Approaches with Gaussian Processes

Ricardo Andrade-Pacheco, James Hensman, Neil D. Lawrence

Proceedings of the Seventeenth International Workshop 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-continous data, discriminative classification with missing inputs and manifold learning informed by class labels.

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Cite this Paper

BibTeX


@InProceedings{Andrade-hybrid14,
  title = 	 {Hybrid Discriminative-Generative Approaches with Gaussian Processes},
  author = 	 {Andrade-Pacheco, Ricardo and Hensman, James and Lawrence, Neil D.},
  booktitle = 	 {Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics},
  pages = 	 {47--56},
  year = 	 {2014},
  editor = 	 {Kaski, Sami and Corander, Jukka},
  volume = 	 {33},
  address = 	 {Iceland},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v33/andradepacheco14.pdf},
  url = 	 {/publications/andrade-hybrid14.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-continous data, discriminative classification with missing inputs and manifold learning informed by class labels.}
}

Endnote

%0 Conference Paper
%T Hybrid Discriminative-Generative Approaches with Gaussian Processes
%A Ricardo Andrade-Pacheco
%A James Hensman
%A Neil D. Lawrence
%B Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics
%D 2014
%E Sami Kaski
%E Jukka Corander	
%F Andrade-hybrid14
%I PMLR
%P 47--56
%U /publications/andrade-hybrid14.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-continous data, discriminative classification with missing inputs and manifold learning informed by class labels.

RIS


TY  - CPAPER
TI  - Hybrid Discriminative-Generative Approaches with Gaussian Processes
AU  - Ricardo Andrade-Pacheco
AU  - James Hensman
AU  - Neil D. Lawrence
BT  - Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics
DA  - 2014/04/02
ED  - Sami Kaski
ED  - Jukka Corander	
ID  - Andrade-hybrid14
PB  - PMLR
VL  - 33
SP  - 47
EP  - 56
L1  - http://proceedings.mlr.press/v33/andradepacheco14.pdf
UR  - /publications/andrade-hybrid14.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-continous data, discriminative classification with missing inputs and manifold learning informed by class labels.
ER  -

APA


Andrade-Pacheco, R., Hensman, J. & Lawrence, N.D.. (2014). Hybrid Discriminative-Generative Approaches with Gaussian Processes. Proceedings of the Seventeenth International Workshop on Artificial Intelligence and Statistics 33:47-56 Available from /publications/andrade-hybrid14.html.