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» Messy Genetic Algorithms for Subset Feature Selection
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IDA
2002
Springer
13 years 8 months ago
Evolutionary model selection in unsupervised learning
Feature subset selection is important not only for the insight gained from determining relevant modeling variables but also for the improved understandability, scalability, and pos...
YongSeog Kim, W. Nick Street, Filippo Menczer
CVPR
2012
IEEE
11 years 11 months ago
Beyond spatial pyramids: Receptive field learning for pooled image features
In this paper we examine the effect of receptive field designs on classification accuracy in the commonly adopted pipeline of image classification. While existing algorithms us...
Yangqing Jia, Chang Huang, Trevor Darrell
BIBE
2007
IEEE
136views Bioinformatics» more  BIBE 2007»
13 years 10 months ago
A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR
Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes ...
Yi Zhang, Chris H. Q. Ding, Tao Li
MTA
2007
115views more  MTA 2007»
13 years 8 months ago
Video scene retrieval with interactive genetic algorithm
This paper proposes a video scene retrieval algorithm based on emotion. First, abrupt/gradual shot boundaries are detected in the video clip of representing a specific story. Then,...
Hun-Woo Yoo, Sung-Bae Cho
IBPRIA
2003
Springer
14 years 1 months ago
Active Region Segmentation of Mammographic Masses Based on Texture, Contour and Shape Features
Abstract. In this paper we propose a supervised method for the segmentation of masses in mammographic images. The algorithm starts with a selected pixel inside the mass, which has ...
Joan Martí, Jordi Freixenet, Xavier Mu&ntil...