Hybrid Discriminative-Generative Approach with Gaussian Processes
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
Text
Pacheco et al. "Hybrid Discriminative-Generative Approach with Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2014.Markdown
[Pacheco et al. "Hybrid Discriminative-Generative Approach with Gaussian Processes." International Conference on Artificial Intelligence and Statistics, 2014.](https://mlanthology.org/aistats/2014/pacheco2014aistats-hybrid/)BibTeX
@inproceedings{pacheco2014aistats-hybrid,
title = {{Hybrid Discriminative-Generative Approach with Gaussian Processes}},
author = {Pacheco, Ricardo Andrade and Hensman, James and Zwiessele, Max and Lawrence, Neil D.},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2014},
pages = {47-56},
url = {https://mlanthology.org/aistats/2014/pacheco2014aistats-hybrid/}
}