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» A selective sampling approach to active feature selection
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FUIN
2010
268views more  FUIN 2010»
13 years 3 months ago
Boruta - A System for Feature Selection
Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irr...
Miron B. Kursa, Aleksander Jankowski, Witold R. Ru...
ICANN
2010
Springer
13 years 7 months ago
The Support Feature Machine for Classifying with the Least Number of Features
We propose the so-called Support Feature Machine (SFM) as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyperplan...
Sascha Klement, Thomas Martinetz
ACMICEC
2007
ACM
117views ECommerce» more  ACMICEC 2007»
14 years 1 months ago
Selectively acquiring ratings for product recommendation
Accurate prediction of customer preferences on products is the key to any recommender systems to realize its promised strategic values such as improved customer satisfaction and t...
Zan Huang
HIS
2007
13 years 10 months ago
Active Selection of Training Examples for Meta-Learning
Meta-Learning has been used to relate the performance of algorithms and the features of the problems being tackled. The knowledge in Meta-Learning is acquired from a set of meta-e...
Ricardo Bastos Cavalcante Prudêncio, Teresa ...
PAMI
2010
192views more  PAMI 2010»
13 years 7 months ago
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
—A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based on primal-dual lea...
Carlos Alzate, Johan A. K. Suykens