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ICML
2008
IEEE
14 years 9 months ago
An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
We show that linear value-function approximation is equivalent to a form of linear model approximation. We then derive a relationship between the model-approximation error and the...
Ronald Parr, Lihong Li, Gavin Taylor, Christopher ...
ICML
2009
IEEE
14 years 9 months ago
Partially supervised feature selection with regularized linear models
This paper addresses feature selection techniques for classification of high dimensional data, such as those produced by microarray experiments. Some prior knowledge may be availa...
Thibault Helleputte, Pierre Dupont
KDD
2004
ACM
139views Data Mining» more  KDD 2004»
14 years 9 months ago
Learning a complex metabolomic dataset using random forests and support vector machines
Metabolomics is the omics science of biochemistry. The associated data include the quantitative measurements of all small molecule metabolites in a biological sample. These datase...
Young Truong, Xiaodong Lin, Chris Beecher
ICML
2006
IEEE
14 years 9 months ago
Feature subset selection bias for classification learning
Feature selection is often applied to highdimensional data prior to classification learning. Using the same training dataset in both selection and learning can result in socalled ...
Surendra K. Singhi, Huan Liu
AI
2010
Springer
14 years 1 months ago
Supervised Machine Learning for Summarizing Legal Documents
This paper presents a supervised machine learning approach for summarizing legal documents. A commercial system for the analysis and summarization of legal documents provided us wi...
Mehdi Yousfi Monod, Atefeh Farzindar, Guy Lapalme