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ICML
2010
IEEE
13 years 9 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
KDD
2008
ACM
246views Data Mining» more  KDD 2008»
14 years 8 months ago
Direct mining of discriminative and essential frequent patterns via model-based search tree
Frequent patterns provide solutions to datasets that do not have well-structured feature vectors. However, frequent pattern mining is non-trivial since the number of unique patter...
Wei Fan, Kun Zhang, Hong Cheng, Jing Gao, Xifeng Y...
AUSAI
2005
Springer
14 years 1 months ago
Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
Peter Vamplew, Robert Ollington
VTC
2007
IEEE
119views Communications» more  VTC 2007»
14 years 2 months ago
Adaptive Resource Allocation in Cellular OFDMA System with Multiple Relay Stations
— We address the problem of radio resource allocation in the Downlink (DL) of a cellular system with Relay Stations (RS), based on Orthogonal Frequency Division Multiple Access (...
Megumi Kaneko, Petar Popovski
ICPR
2008
IEEE
14 years 2 months ago
Incremental clustering via nonnegative matrix factorization
Nonnegative matrix factorization (NMF) has been shown to be an efficient clustering tool. However, NMF`s batch nature necessitates recomputation of whole basis set for new samples...
Serhat Selcuk Bucak, Bilge Günsel