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TCBB
2010
176views more  TCBB 2010»
13 years 6 months ago
Feature Selection for Gene Expression Using Model-Based Entropy
—Gene expression data usually contain a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best...
Shenghuo Zhu, Dingding Wang, Kai Yu, Tao Li, Yihon...
ICANN
2005
Springer
14 years 1 months ago
Multiresponse Sparse Regression with Application to Multidimensional Scaling
Sparse regression is the problem of selecting a parsimonious subset of all available regressors for an efficient prediction of a target variable. We consider a general setting in w...
Timo Similä, Jarkko Tikka
SIGIR
2002
ACM
13 years 7 months ago
Document clustering with cluster refinement and model selection capabilities
In this paper, we propose a document clustering method that strives to achieve: (1) a high accuracy of document clustering, and (2) the capability of estimating the number of clus...
Xin Liu, Yihong Gong, Wei Xu, Shenghuo Zhu
JMLR
2002
100views more  JMLR 2002»
13 years 7 months ago
On the Convergence of Optimistic Policy Iteration
We consider a finite-state Markov decision problem and establish the convergence of a special case of optimistic policy iteration that involves Monte Carlo estimation of Q-values,...
John N. Tsitsiklis
ICCV
2011
IEEE
12 years 7 months ago
Informative Feature Selection for Object Recognition via Sparse PCA
Bag-of-words (BoW) methods are a popular class of object recognition methods that use image features (e.g., SIFT) to form visual dictionaries and subsequent histogram vectors to r...
Nikhil Naikal, Allen Y. Yang, S. Shankar Sastry