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JMLR
2012
13 years 9 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
ESOP
2001
Springer
15 years 11 months ago
The Def-inite Approach to Dependency Analysis
Abstract. We propose a new representation for the domain of Definite Boolean functions. The key idea is to view the set of models of a Boolean function as an incidence relation be...
Samir Genaim, Michael Codish
IJCAI
2007
15 years 8 months ago
Reinforcement Learning of Local Shape in the Game of Go
We explore an application to the game of Go of a reinforcement learning approach based on a linear evaluation function and large numbers of binary features. This strategy has prov...
David Silver, Richard S. Sutton, Martin Mülle...
MP
1998
108views more  MP 1998»
15 years 6 months ago
Base polytopes of series-parallel posets: Linear description and optimization
We define the base polytope B(P, g) of a partially ordered set P and a supermodular function g on the ideals ofP as the convex hull of the incidence vectors of all linear extensio...
Rainer Schrader, Andreas S. Schulz, Georg Wambach
ICASSP
2011
IEEE
14 years 10 months ago
Soft frame margin estimation of Gaussian Mixture Models for speaker recognition with sparse training data
—Discriminative Training (DT) methods for acoustic modeling, such as MMI, MCE, and SVM, have been proved effective in speaker recognition. In this paper we propose a DT method fo...
Yan Yin, Qi Li