NDLI: Beam search for feature selection in automatic SVM defect classification

Content Provider	IEEE Xplore Digital Library
Author	Gupta, P. Doermann, D. DeMenthon, D.
Copyright Year	2002
Description	Author affiliation: Language & Media Process. Lab., Maryland Univ., College Park, MD, USA (Gupta, P.; Doermann, D.; DeMenthon, D.)
Abstract	Often in pattern classification problems, one tries to extract a large number of features and base the classifier decision on as much information as possible. This yields an array of features that are 'potentially' useful. Most of the time however, large feature sets are sub-optimal in describing the samples since they tend to over-represent the data and model noise along with the useful information in the data. Selecting relevant features from the available set of features is, therefore, a challenging task. In this paper, we present an innovative feature selection algorithm called Smart Beam Search (SBS), which is used with a support vector machine (SVM) based classifier for automatic defect classification. This feature selection approach not only reduces the dimensionality of the feature space substantially, but also improves the classifier performance.
Starting Page	212
Ending Page	215
File Size	294917
Page Count	4
File Format	PDF
ISBN	076951695X
ISSN	10514651
DOI	10.1109/ICPR.2002.1048275
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-08-11
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Frequency selective surfaces Data mining Filters Laboratories Educational institutions Pattern classification Pattern recognition Image recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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