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Hybrid of Generative and Discriminative Models
If you have a question about this talk, please contact Shakir Mohamed.
In this RCC , I will cover the topic of combining generative and discriminative learning approaches for classification.I will start by covering a classic:
This paper is unfortunately not an easy read, but it is one of the most cited on the topic of comparing generative and discriminative learning. The analysis is somewhat specific, but still provides interesting insight.I will then compare the two hybrid generative / discriminative approaches proposed in the following two papers (the first one being more Bayesian; the second one being more Frequentist):
If you have time to read only one paper, read the Bishop’s one (I will focus more on it) and skim through the other two. A slightly shorter conference version is available here .
If you are quite interested on the topic and you want more details, have a look at the PhD thesis of Julia Lassere and chapter 5 of the PhD thesis of Guillaume Bouchard (chapter 5 is in English even though the several other chapters are in French).
This talk is part of the Machine Learning Reading Group @ CUED series.
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