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ICML
2010
IEEE
13 years 11 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
CSSC
2008
80views more  CSSC 2008»
13 years 10 months ago
Logistic Discrimination with Total Variation Regularization
This article introduces a regularized logistic discrimination method that is especially suited for discretized stochastic processes (such as periodograms, spectrograms, EEG curves...
Robin Rühlicke, Daniel Gervini
BC
2004
65views more  BC 2004»
13 years 9 months ago
SINBAD: A neocortical mechanism for discovering environmental variables and regularities hidden in sensory input
We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environme...
Oleg V. Favorov, Dan Ryder
ICML
2005
IEEE
14 years 10 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
UAI
2008
13 years 11 months ago
Feature Selection via Block-Regularized Regression
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one...
Seyoung Kim, Eric P. Xing