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Paper Abstract and Keywords
Presentation 2007-03-14 10:10
Unbiased Learning for Hierarchical Models
Masashi Sekino, Katsumi Nitta (Tokyo Tech)
Abstract (in Japanese) (See Japanese page) 
(in English) It is known that overfitting occurs when a conventional statistical learning method such as maximum likelihood estimation, maximum a posteriori estimation or Bayesian estimation is applied to hierarchical models. In this paper, we clarify the cause of why overfitting occurs when a conventional statistical learning method is applied to hierarchical models, and propose “Unbiased Learning” which is a learning framework based on unbiased likelihood (information criterion) for hierarchical models. We confirm the effectiveness of unbiased learning when applied to kernel regression model.
Keyword (in Japanese) (See Japanese page) 
(in English) hierarchical model / statistical learning / information criterion / model selection / overfitting / / /  
Reference Info. IEICE Tech. Rep., vol. 106, no. 588, NC2006-136, pp. 109-114, March 2007.
Paper # NC2006-136 
Date of Issue 2007-03-07 (NC) 
ISSN Print edition: ISSN 0913-5685
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Conference Information
Committee NC  
Conference Date 2007-03-14 - 2007-03-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Tamagawa University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NC 
Conference Code 2007-03-NC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Unbiased Learning for Hierarchical Models 
Sub Title (in English)  
Keyword(1) hierarchical model  
Keyword(2) statistical learning  
Keyword(3) information criterion  
Keyword(4) model selection  
Keyword(5) overfitting  
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1st Author's Name Masashi Sekino  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Katsumi Nitta  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Speaker Author-1 
Date Time 2007-03-14 10:10:00 
Presentation Time 20 minutes 
Registration for NC 
Paper # NC2006-136 
Volume (vol) vol.106 
Number (no) no.588 
Page pp.109-114 
#Pages
Date of Issue 2007-03-07 (NC) 


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