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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...
FGR
2008
IEEE
346views Biometrics» more  FGR 2008»
14 years 2 months ago
Markov random field models for hair and face segmentation
This paper presents an algorithm for measuring hair and face appearance in 2D images. Our approach starts by using learned mixture models of color and location information to sugg...
Kuang-chih Lee, Dragomir Anguelov, Baris Sumengen,...
ICA
2007
Springer
14 years 2 months ago
Discovering Convolutive Speech Phones Using Sparseness and Non-negativity
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can b...
Paul D. O'Grady, Barak A. Pearlmutter
EMNLP
2007
13 years 9 months ago
What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
This paper presents a syntax-driven approach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than using syntactic fea...
Mengqiu Wang, Noah A. Smith, Teruko Mitamura
NIPS
2008
13 years 9 months ago
Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
Zhihua Zhang, Michael I. Jordan, Dit-Yan Yeung