Hybrid Discriminative-Generative Approaches with Gaussian Processes

Ricardo Andrade-PachecoJames HensmanNeil D. Lawrence
,  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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-andrade-hybrid14, title = {Hybrid Discriminative-Generative Approaches with Gaussian Processes}, author = {Ricardo Andrade-Pacheco and James Hensman and Neil D. Lawrence}, pages = {47--56}, year = {}, editor = {}, volume = {33}, address = {Iceland}, url = {http://inverseprobability.com/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 %C Proceedings of Machine Learning Research %D %E %F pmlr-v-andrade-hybrid14 %I PMLR %J Proceedings of Machine Learning Research %P 47--56 %U http://inverseprobability.com %V %W PMLR %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 - PY - DA - ED - ID - pmlr-v-andrade-hybrid14 PB - PMLR SP - 47 DP - PMLR EP - 56 L1 - UR - http://inverseprobability.com/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.. (). Hybrid Discriminative-Generative Approaches with Gaussian Processes. , in PMLR :47-56

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