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» Feature Subset Selection Using a Genetic Algorithm
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CVPR
2006
IEEE
14 years 11 months ago
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
We present a method to automatically learn object categories from unlabeled images. Each image is represented by an unordered set of local features, and all sets are embedded into...
Kristen Grauman, Trevor Darrell
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
TREC
2004
13 years 10 months ago
Feature Generation, Feature Selection, Classifiers, and Conceptual Drift for Biomedical Document Triage
We approached the problem of classifying papers for the TREC 2004 Genomics Track triage task as a four step process: feature generation, feature selection, classifier training, an...
Aaron M. Cohen, Ravi Teja Bhupatiraju, William R. ...
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 2 months ago
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure o...
Yung-Keun Kwon, Byung Ro Moon
CCE
2008
13 years 9 months ago
Bidirectional branch and bound for controlled variable selection: Part I. Principles and minimum singular value criterion
The minimum singular value (MSV) rule is a useful tool for selecting controlled variables (CVs) from the available measurements. However, the application of the MSV rule to large-...
Yi Cao, Vinay Kariwala