2004
A meta-learning method to select the kernel width in Support Vector Regression
SOARES, Carlos; Pavel BRAZDIL a Petr KUBAZákladní údaje
Originální název
A meta-learning method to select the kernel width in Support Vector Regression
Název česky
Metoda meta-učení pro volbu šířky jádra
Autoři
SOARES, Carlos (620 Portugalsko); Pavel BRAZDIL (826 Velká Británie a Severní Irsko) a Petr KUBA (203 Česká republika, garant)
Vydání
Machine Learning Journal, Netherlands, Kluwer Academic Publishers, 2004, 0885-6125
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Nizozemské království
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 3.258
Kód RIV
RIV/00216224:14330/04:00010093
Organizační jednotka
Fakulta informatiky
UT WoS
000188925100002
Klíčová slova anglicky
meta-learning; parameter setting; support vector machines; Gaussian kernel; learning rankings
Štítky
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 22. 4. 2009 16:19, prof. Ing. Jiří Sochor, CSc.
V originále
The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.
Česky
The Support Vector Machine algorithm is sensitive to the choice of parameter settings. If these are not set correctly, the algorithm may have a substandard performance. Suggesting a good setting is thus an important problem. We propose a meta-learning methodology for this purpose and exploit information about the past performance of different settings. The methodology is applied to set the width of the Gaussian kernel. We carry out an extensive empirical evaluation, including comparisons with other methods (fixed default ranking; selection based on cross-validation and a heuristic method commonly used to set the width of the SVM kernel). We show that our methodology can select settings with low error while providing significant savings in time. Further work should be carried out to see how the methodology could be adapted to different parameter setting tasks.
Návaznosti
MSM 143300003, záměr |
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