An iterative algorithm learning the maximal margin classifier

Pattern Recognition

Volumn 36, Issue 9, 2003, Pages 1985-1996

  Franc, Vojtěch ^a   Hlaváč, Václav ^a  

^a CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

Author keywords

Kernel functions; Linear classifier; Pattern recognition; Supervised learning; Support vector machines

Indexed keywords

ALGORITHMS; ITERATIVE METHODS; VECTORS;

MAXIMAL MARGIN CLASSIFIERS;

PATTERN RECOGNITION;

PATTERN RECOGNITION;

EID: 0038647819     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(03)00060-8     Document Type: Article

References (18)

1. Vapnik V.N. Chervonenkis A.Ya. The Theory of Pattern Recognition 1974 Nauka Moscow
2. Vapnik V.N. The Nature of Statistical Learning Theory 1995 Springer Berlin
3. Schlesinger M.I. Kalmykov V.G. Suchorukov A.A. Sravnitelnyj analiz algoritmov sinteza linejnogo reshajushchego pravila dlja proverki slozhnych gipotez (Comparative analysis of algorithms synthesising linear decision rule for analysis of complex hypotheses) Automatika 1 1981 3-9 (in Russian)
4. Kozinec B.N. Rekurentnyj algoritm razdelenia vypuklych obolochek dvuch mnozhestv (Recurrent algorithm separating convex hulls of two sets) Vapnik V.N. Algoritmy Obuchenia Raspoznavania (Learning algorithms in pattern recognition) 1973 43-50 Sovetskoje radio Moskva (in Russian)
5. Schlesinger M.I. Hlaváč V. Ten Lectures on Statistical and Structural Pattern Recognition 2002 Kluwer Academic Publishers Dordrecht
6. Aizerman M.A. Braverman E.M. Rozoner L.I. Theoretical foundations of the potential function method in pattern recognition learning Autom. Remote Control 25 1964 821-837
7. Platt J.C. Fast training of support vectors machines using sequential minimal optimization Scholkopf B. Burges C.J.C. Smola A.J. Advances in Kernel Methods 1998 MIT Press Cambridge, MA, USA
8. T. Joachims, Advances in Kernel methods-support vector learning, in: B. Schlkopf, C. Burges, A. Smola (Eds.), Chapter Making Large-Scale Support Vector Machine Learning Practical, MIT Press, Cambridge, MA, 1999, pp. 169-184. Code downloadable from http://svmlight.joachims.org
9. Yi Li, P.M. Long, The relaxed online maximum margin algorithm, in: S.A. Solla, T.K. Leen, K.R. Muller (Eds.), Advances in Neural Information Processing Systems, Vol. 12, MIT Press, Cambridge, MA, USA, 2000, pp. 498-504.
10. Keerthi S.S. Shevade S.K. Bhattacharyya C. Murthy K.R.K. A fast iterative nearest point algorithm for support vector machine classifier design IEEE Trans. Neural Networks 11 1 2000 124-136
11. T.T. Friess, R. Harrison, Support vector neural networks: the Kernel Adatron with bias and soft-margin, Technical Report 725, University of Sheffield, UK, 1998.
12. Kowalczyk A. Advances in large margin classifiers Smola A.J. Bartlett P.J. Schõlkopf B. Schuurmans D. Chapter, Maximal Margin Perceptron 1999 MIT Press Cambridge, MA
13. A. Novikoff, On convergence proofs for perceptrons, in: Proceeding of the Symposium on the Mathematical Theory of Automata, Polytechnic Institute of Brooklyn Vol. 12, 1963, pp. 615-622.
14. Gilbert E.G. Minimizing the quadratic form on a convex set SIAM J. Control 4 1966 61-79
15. Mitchell B.F. Demyanov V.F. Malozemov V.N. Finding the point of a polyhedron closest to the origin SIAM J. Control 12 1974 19-26
16. Ripley B.D. Neural networks and related methods for classification (with discussion) J. R. Statist. Soc. Ser. B 56 1994 409-456
17. UCI-benchmark repository of artificial and real data sets, University of California Irvine, http://www.ics.uci.edu/~mlearn
18. Kresel U. Advances in Kernel methods-support vector learning Schölkopf B. Burges C. Smola A. Chapter Pairwise classification and support vector machines 1999 255-268 MIT Press Cambridge, MA