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ICML
2007
IEEE
15 years 10 months ago
Supervised feature selection via dependence estimation
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
Le Song, Alex J. Smola, Arthur Gretton, Karsten M....
WAPCV
2007
Springer
15 years 4 months ago
Modeling the Dynamics of Feature Binding During Object-Selective Attention
We present a biologically plausible computational model for solving the visual feature binding problem. The binding problem appears to be due to the distributed nature of visual pr...
Albert L. Rothenstein, John K. Tsotsos
SSPR
2004
Springer
15 years 3 months ago
Feature Subset Selection Using an Optimized Hill Climbing Algorithm for Handwritten Character Recognition
This paper presents an optimized Hill Climbing algorithm to select a subset of features for handwritten character recognition. The search is conducted taking into account a random ...
Carlos M. Nunes, Alceu de Souza Britto Jr., Celso ...
TNN
2008
119views more  TNN 2008»
14 years 10 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal
FUZZIEEE
2007
IEEE
15 years 4 months ago
Distance Measure Assisted Rough Set Feature Selection
Abstract— Feature Selection (FS) is a technique for dimensionality reduction. Its aims are to select a subset of the original features of a dataset which are rich in the most use...
Neil MacParthalain, Qiang Shen, Richard Jensen