## Discriminative, Generative and Imitative Learning (2002)

Citations: | 34 - 1 self |

### BibTeX

@MISC{Jebara02discriminative,generative,

author = {Tony Jebara},

title = {Discriminative, Generative and Imitative Learning},

year = {2002}

}

### Years of Citing Articles

### OpenURL

### Abstract

I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars.