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PRL
1998
132views more  PRL 1998»
13 years 8 months ago
Unsupervised feature selection using a neuro-fuzzy approach
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature evaluation index with unsupervised training. The concept of a ¯exible membersh...
Jayanta Basak, Rajat K. De, Sankar K. Pal
JMLR
2010
116views more  JMLR 2010»
13 years 3 months ago
Feature Selection, Association Rules Network and Theory Building
As the size and dimensionality of data sets increase, the task of feature selection has become increasingly important. In this paper we demonstrate how association rules can be us...
Sanjay Chawla
KDD
2003
ACM
195views Data Mining» more  KDD 2003»
14 years 9 months ago
Visualizing changes in the structure of data for exploratory feature selection
Using visualization techniques to explore and understand high-dimensional data is an efficient way to combine human intelligence with the immense brute force computation power ava...
Elias Pampalk, Werner Goebl, Gerhard Widmer
CVPR
2005
IEEE
14 years 11 months ago
Tracking Non-Stationary Appearances and Dynamic Feature Selection
Since the appearance changes of the target jeopardize visual measurements and often lead to tracking failure in practice, trackers need to be adaptive to non-stationary appearance...
Ming Yang, Ying Wu
ICML
1994
IEEE
14 years 20 days ago
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the te...
David B. Skalak