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» Feature Selection in Clustering Problems
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CORR
2006
Springer
130views Education» more  CORR 2006»
15 years 2 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
TASLP
2011
14 years 9 months ago
Time-Frequency Cepstral Features and Heteroscedastic Linear Discriminant Analysis for Language Recognition
Abstract—The shifted delta cepstrum (SDC) is a widely used feature extraction for language recognition (LRE). With a high context width due to incorporation of multiple frames, S...
Weiqiang Zhang, Liang He, Yan Deng, Jia Liu, M. T....
CVPR
2004
IEEE
16 years 4 months ago
Video Data Mining Using Configurations of Viewpoint Invariant Regions
We describe a method for obtaining the principal objects, characters and scenes in a video by measuring the reoccurrence of spatial configurations of viewpoint invariant features....
Josef Sivic, Andrew Zisserman
BMCBI
2006
198views more  BMCBI 2006»
15 years 2 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...
ICDM
2008
IEEE
122views Data Mining» more  ICDM 2008»
15 years 9 months ago
Nonnegative Matrix Factorization for Combinatorial Optimization: Spectral Clustering, Graph Matching, and Clique Finding
Nonnegative matrix factorization (NMF) is a versatile model for data clustering. In this paper, we propose several NMF inspired algorithms to solve different data mining problems....
Chris H. Q. Ding, Tao Li, Michael I. Jordan