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ICML
2004
IEEE
16 years 7 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
ICML
2003
IEEE
16 years 7 months ago
Relational Instance Based Regression for Relational Reinforcement Learning
Relational reinforcement learning (RRL) is a Q-learning technique which uses first order regression techniques to generalize the Qfunction. Both the relational setting and the Q-l...
Kurt Driessens, Jan Ramon
STOC
2003
ACM
122views Algorithms» more  STOC 2003»
16 years 7 months ago
Learning juntas
We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function which depends on an unknown set of k out of n Boolean variables. We give...
Elchanan Mossel, Ryan O'Donnell, Rocco A. Servedio
ALT
2007
Springer
16 years 3 months ago
Learning Rational Stochastic Tree Languages
Abstract. We consider the problem of learning stochastic tree languages, i.e. probability distributions over a set of trees T(F), from a sample of trees independently drawn accordi...
François Denis, Amaury Habrard
ICASSP
2009
IEEE
16 years 1 months ago
A variational EM algorithm for learning eigenvoice parameters in mixed signals
We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a pri...
Ron J. Weiss, Daniel P. W. Ellis