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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
9 years 3 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
AAAI
2008
8 years 11 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
IJCNN
2007
IEEE
9 years 3 months ago
Search Strategies Guided by the Evidence for the Selection of Basis Functions in Regression
— This work addresses the problem of selecting a subset of basis functions for a model linear in the parameters for regression tasks. Basis functions from a set of candidates are...
Ignacio Barrio, Enrique Romero, Lluís Belan...
CVPR
2005
IEEE
9 years 11 months ago
A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation
We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure f...
Shaorong Chang, Nilanjan Dasgupta, Lawrence Carin
CSDA
2007
134views more  CSDA 2007»
8 years 9 months ago
Variational approximations in Bayesian model selection for finite mixture distributions
Variational methods for model comparison have become popular in the neural computing/machine learning literature. In this paper we explore their application to the Bayesian analys...
Clare A. McGrory, D. M. Titterington
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