Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
In spite of the popularity of probabilistic mixture models for latent structure discovery from data, mixture models do not have a natural mechanism for handling sparsity, where ea...
A new method for function estimation and variable selection, specifically designed for additive models fitted by cubic splines is proposed.This new method involves regularizing ...
Marta Avalos, Yves Grandvalet, Christophe Ambroise
Abstract. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and contextfree languages to, in principle...
Statistical language models estimate the probability of a word occurring in a given context. The most common language models rely on a discrete enumeration of predictive contexts ...
John Blitzer, Kilian Q. Weinberger, Lawrence K. Sa...