@conference {13513,
	title = {Beam Search for Feature Selection in Automatic SVMDefect Classification},
	booktitle = {International Conference on Pattern Recognition},
	year = {2002},
	month = {2002///},
	pages = {212 - 215},
	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 {\textquoteright}potentially{\textquoteright} 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 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.},
	author = {Gupta,P. and David Doermann and DeMenthon,D.}
}
