Various hyperplane classifiers using kernel feature spaces
In this paper we introduce a new family of hyperplane classifiers. But, in contrast to Support Vector Machines (SVM) - where a constrained quadratic optimization is used - some of the proposed methods lead to the unconstrained minimization of convex functions while others merely require solving a li...
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| Dokumentumtípus: | Cikk |
| Megjelent: |
2003
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| Sorozat: | Acta cybernetica
16 No. 2 |
| Kulcsszavak: | Számítástechnika, Kibernetika |
| Tárgyszavak: | |
| Online Access: | http://acta.bibl.u-szeged.hu/12722 |
| LEADER | 01463nab a2200241 i 4500 | ||
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| 008 | 161015s2003 hu o 0|| eng d | ||
| 022 | |a 0324-721X | ||
| 040 | |a SZTE Egyetemi Kiadványok Repozitórium |b hun | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kovács Kornél | |
| 245 | 1 | 0 | |a Various hyperplane classifiers using kernel feature spaces |h [elektronikus dokumentum] / |c Kovács Kornél |
| 260 | |c 2003 | ||
| 300 | |a 271-278 | ||
| 490 | 0 | |a Acta cybernetica |v 16 No. 2 | |
| 520 | 3 | |a In this paper we introduce a new family of hyperplane classifiers. But, in contrast to Support Vector Machines (SVM) - where a constrained quadratic optimization is used - some of the proposed methods lead to the unconstrained minimization of convex functions while others merely require solving a linear system of equations. So that the efficiency of these methods could be checked, classification tests were conducted on standard databases. In our evaluation, classification results of SVM were of course used as a general point of reference, which we found were outperformed in many cases. | |
| 650 | 4 | |a Természettudományok | |
| 650 | 4 | |a Számítás- és információtudomány | |
| 695 | |a Számítástechnika, Kibernetika | ||
| 700 | 0 | 1 | |a Kocsor András |e aut |
| 710 | |a Conference for PhD Students in Computer Science (3.) (2002) (Szeged) | ||
| 856 | 4 | 0 | |u http://acta.bibl.u-szeged.hu/12722/1/cybernetica_016_numb_002_271-278.pdf |z Dokumentum-elérés |