We establish a mistake bound for an ensemble method for classification based on maximizing the entropy of voting weights subject to margin constraints. The bound is the same as a ...
Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representat...
A new approach to evolutionary computation, called Learnable Evolution Model (LEM), has been applied to the problem of optimizing tube structures of heat exchangers. In contrast t...
The lack of discipline and consistency in gene naming poses a formidable challenge to researchers in locating relevant information sources in the genomics literature. The research...
Mehmet Kayaalp, Alan R. Aronson, Susanne M. Humphr...