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BMCBI
2010

Unifying generative and discriminative learning principles

8 years 8 months ago
Unifying generative and discriminative learning principles
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-discriminative trade-off learning principles as special cases, and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites. Conclusions: We find that the proposed learning principle he...
Jens Keilwagen, Jan Grau, Stefan Posch, Marc Stric
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Jens Keilwagen, Jan Grau, Stefan Posch, Marc Strickert, Ivo Grosse
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