Linear Separability: Quasisecant Method and Application To Semi-Supervised Data Classification
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Date
2010
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Vilnius Gediminas Technical University
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Abstract
In this paper we have proposed a semi-supervised algorithm based on quasisecant optimization method for solving data classification problems. The algorithm computes hyperplane(s) to separate two sets with respect to some tolerance. An error function is formulated and an algorithm for its minimization is expressed. We present results of numerical experiments using several UCI test data sets and compare the proposed algorithm with two supervised data classification algorithm (linear separability, max-min separability) and two support vector machine solvers (LIBSVM and SVM-light). © Izmir University of Economics, Turkey, 2010.
Description
24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010 -- 23 June 2010 through 26 June 2010 -- Izmir -- 106702
Keywords
Nonsmooth optimization, Quasisecant method, Semi-supervised data classification, Algorithms, Optimization, Support vector machines, Data classification, Data classification problems, Linear separability, Nonsmooth optimization, Numerical experiments, Optimization method, Quasisecant method, Semi-supervised algorithm, Classification (of information)
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Source
24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010
Volume
Issue
Start Page
264
End Page
269
