Model Selection in Kernel Methods Based on a Spectral Analysis of Label Information

Author(s):  
Mikio L. Braun ◽  
Tilman Lange ◽  
Joachim M. Buhmann
2014 ◽  
Vol 25 (10) ◽  
pp. 1879-1893 ◽  
Author(s):  
Di You ◽  
Carlos Fabian Benitez-Quiroz ◽  
Aleix M. Martinez

Author(s):  
Khanh Nguyen

Max-margin and kernel methods are dominant approaches to solve many tasks in machine learning. However, the paramount question is how to solve model selection problem in these methods. It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications. Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and online setting.


1977 ◽  
Vol 8 (1) ◽  
pp. 134-150 ◽  
Author(s):  
James E. Reinmuth ◽  
Michael D. Geurts

2008 ◽  
Author(s):  
Ji Ha Lee ◽  
Sung Won Choi ◽  
Ji Sun Min ◽  
Eun Ju Jaekal ◽  
Gyhye Sung

1953 ◽  
Author(s):  
C. J. Burke ◽  
R. Narasimhan ◽  
O. J. Benepe

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