Resilient approximation of kernel classifiers
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Trained support vector machines (SVMs) have a slow run-time classification speed if the classification problem is noisy and the sample data set is large. Approximating the SVM by a more sparse function has been proposed to solve to this problem. In this study, different variants of approximation algorithms are empirically compared. It is shown that gradient descent using the improved Rprop algorithm increases the robustness of the method compared to fixed-point iteration. Three different heuristics for selecting the support vectors to be used in the construction of the sparse approximation are proposed. It turns out that none is superior to random selection. The effect of a finishing gradient descent on all parameters of the sparse approximation is studied.
Original language | English |
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Title of host publication | Artificial Neural Networks – ICANN 2007 : 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I |
Editors | Joaquim Marques de Sá, Lius A. Alexandre, Włodzisław Duch, Danilo Mandic |
Number of pages | 10 |
Volume | Part I |
Publisher | Springer |
Publication date | 2007 |
Pages | 139-148 |
ISBN (Print) | 978-3-540-74689-8 |
ISBN (Electronic) | 978-3-540-74690-4 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Event | 17th International Conference on Artificial Neural Networks, ICANN 2007 - Porto, Portugal Duration: 9 Sep 2007 → 13 Sep 2007 |
Conference
Conference | 17th International Conference on Artificial Neural Networks, ICANN 2007 |
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Land | Portugal |
By | Porto |
Periode | 09/09/2007 → 13/09/2007 |
Series | Lecture notes in computer science |
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Volume | 4668 |
ISSN | 0302-9743 |
ID: 168563567