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DAWAK
2010
Springer
15 years 6 months ago
Modelling Complex Data by Learning Which Variable to Construct
Abstract. This paper addresses a task of variable selection which consists in choosing a subset of variables that is sufficient to predict the target label well. Here instead of tr...
Françoise Fessant, Aurélie Le Cam, M...
JMLR
2006
103views more  JMLR 2006»
15 years 6 months ago
MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals
In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene ...
Dana Pe'er, Amos Tanay, Aviv Regev
NC
2006
132views Neural Networks» more  NC 2006»
15 years 6 months ago
Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate Time Series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is...
Stephen Swift, Joost N. Kok, Xiaohui Liu
TKDE
2008
195views more  TKDE 2008»
15 years 6 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
IJAMCIGI
2010
90views more  IJAMCIGI 2010»
15 years 3 months ago
A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
Hyper-heuristics are identified as the methodologies that search the space generated by a finite set of low level heuristics for solving difficult problems. One of the iterative h...
Ender Özcan, Mustafa Misir, Gabriela Ochoa, E...