Unifying generative and discriminative learning principles.

ABSTRACT:

Background

The recognition of functional binding sites in genomic DNA remains one of the fundamental challenges of genome research. During the last decades, a plethora of different and well-adapted models has been developed, but only little attention has been payed to the development of different and similarly well-adapted learning principles. Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications, too.

Results

Here, we propose a generalization of generative and discriminative learning principles containing the maximum likelihood, maximum a posteriori, maximum conditional likelihood, maximum supervised posterior, generative-discriminative trade-off, and penalized generative-discriminati

SUBMITTER: Keilwagen J 

PROVIDER: S-EPMC2848239 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature