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Reference TypeConference Proceedings
Identifier8JMKD3MGPBW34M/3A3H72S
Repositorysid.inpe.br/sibgrapi/2011/07.10.05.55
Metadatasid.inpe.br/sibgrapi/2011/07.10.05.55.06
Sitesibgrapi.sid.inpe.br
Citation KeyMedinaRodriguezHash:2011:CoDiOp
Author1 Medina Rodriguez, Rosario A.
2 Hashimoto, Ronaldo Fumio
Affiliation1 University of Sao Paulo
2 University of Sao Paulo
TitleCombining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers
Conference NameConference on Graphics, Patterns and Images, 24 (SIBGRAPI)
Year2011
EditorLewiner, Thomas
Torres, Ricardo
Book TitleProceedings
DateAug. 28 - 31, 2011
Publisher CityLos Alamitos
PublisherIEEE Computer Society Conference Publishing Services
Conference LocationMaceió
Keywordsstraight line segments, gradient descent technique, dialectical optimization, genetic algorithms, pattern recognition.
AbstractA recent published pattern recognition technique called Straight Line Segment (SLS) uses two sets of straight line segments to classify a set of points from two different classes and it is based on distances between these points and each set of straight line segments. It has been demonstrated that, using this technique, it is possible to generate classifiers which can reach high accuracy rates for supervised pattern classification. However, a critical issue in this technique is to find the optimal positions of the straight line segments given a training data set. This paper proposes a combining method of the dialectical optimization method (DOM) and the gradient descent technique for solving this optimization problem. The main advantage of DOM, such as any evolutionary algorithm, is the capability of escaping from local optimum by multi-point stochastic searching. On the other hand, the strength of gradient descent method is the ability of finding local optimum by pointing the direction that maximizes the objective function. Our hybrid method combines the main characteristics of these two methods. We have applied our combining approach to several data sets obtained from artificial distributions and UCI databases. These experiments show that the proposed algorithm in most cases has higher classification rates with respect to single gradient descent method and the combination of gradient descent with genetic algorithms.
Languageen
Tertiary TypeFull Paper
FormatDVD, On-line.
Size1220 KiB
Number of Files1
Target FileCombining Dialectical Optimization and Gradient Descent Methods for Improving the Accuracy of Straight Line Segment Classifiers.pdf
Last Update2011:07.10.05.55.06 sid.inpe.br/banon/2001/03.30.15.38 rosarior@ime.usp.br
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History2011-07-23 15:36:12 :: rosarior@ime.usp.br -> :: 2011
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