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		<citationkey>Hirata:2005:BiImOp</citationkey>
		<title>Binary image operator design based on stacked generalization</title>
		<format>On-line</format>
		<year>2005</year>
		<date>9-12 Oct. 2005</date>
		<numberoffiles>1</numberoffiles>
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		<author>Hirata, Nina Sumiko Tomita,</author>
		<affiliation>Department of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo,</affiliation>
		<editor>Rodrigues, Maria Andr?ia Formico,</editor>
		<editor>Frery, Alejandro C?sar,</editor>
		<e-mailaddress>nina@ime.usp.br</e-mailaddress>
		<conferencename>Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI)</conferencename>
		<conferencelocation>Natal</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>stacked generalization, image operator design, multi-stage training.</keywords>
		<abstract>Stacked generalization refers to any learning schema that consists of multiple levels of training. Level zero classifiers are those that depend solely on input data while classifiers at other levels may use the output of lower levels as the input. Stacked generalization can be used to  address the difficulties related to the design of image operators defined on large windows. This paper describes a simple stacked generalization schema for the design of binary image operators and presents several application examples that show its effectiveness as a training schema.</abstract>
		<language>en</language>
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