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2005 | 15 | 2 | 295-304
Tytuł artykułu

Strict maximum separability of two finite sets: an algorithmic approach

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents a recursive algorithm for the investigation of a strict,linear separation in the Euclidean space. In the case when sets are linearly separable, it allows us to determine the coefficients of the hyperplanes. An example of using this algorithm as well as its drawbacks are shown. Then the algorithm of determining an optimal separation (in the sense of maximizing the distance between the two sets) is presented.
Rocznik
Tom
15
Numer
2
Strony
295-304
Opis fizyczny
Daty
wydano
2005
otrzymano
2004-04-15
poprawiono
2004-07-09
(nieznana)
2005-01-11
Twórcy
  • Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland
Bibliografia
  • Cristianini N. and Shawe-Taylor J. (2000): An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. - Cambridge: Cambridge University Press.
  • Duda R.O., Hart P.E. and Stork D.G. (1995): Pattern Classification. - New York: Wiley.
  • Franc V. and Hlavač V. (2003): An iterative algorithm learning the maximal margin classifier. - Pattern Recogn., Vol. 36, No. 9, pp. 1985-1996.
  • Jankowski N. (2003): Ontogenic Neural Networks. - Warsaw: EXIT, (in Polish).
  • Jóźwik A. (1975): Method of investigaton of separability of two finite sets in n-dimensional space. - Scientific Works of the Institute of Organization and Manegement, Series: Applied Cybernetics and Computer Science, Vol. 18, (in Polish).
  • Jóźwik A. (1983): A recursive method for the investigation of the linear separability of two sets. - Pattern Recogn., Vol. 16, No. 4, pp. 429-431.
  • Jóźwik A. (1998): Algorithm of investigaton of separability of two sets, prospects of reusing this algorithm to construct the binary classifier. - Proc. 6-th Conf., Networks and Information Systems-Theory, Projects and Applications, Łódź, Poland, pp. 311-316, (in Polish).
  • Kozinec B.N. (1973): Recurrent algorithm separating convex hulls of two sets, In: Learning Algorithms in Pattern Recognition (V.N. Vapnik, Ed.). -Moscow: Soviet Radio, pp. 43-50, (in Russian).
  • Mangasarian O.L. (2000): Generalized Support Vector Machines, Advances in Large Margin classifiers, pp. 135-146, MIT Press, available at ftp://ftp.cs.wisc.edu/math-prog/tech-reports/98-14.ps
  • Vapnik V.N. (2000): The Nature of Statistical Learning Theory. - New York: Springer.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv15i2p295bwm
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