Presentation 2002/12/6
A Proposal of The Neural Network Using Hints on Conditional Quantiles to Improve Its Generalization Performance
Noriyuki YAMANAKA, Ichiro TAKEUCHI, Takeshi FURUHASHI,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) We propose, in this paper, a new approach to improve the generalization performance of artificial neural networks (NN) for regression problems. The basic idea is to incorpolate a robust statistics technique into NN within the framework of learning with hints. In particular, we propose to train NN with the hints on conditional quantiles, and show that this approach yield a robust estimation of conditional expectation, which we want to estimate in regression problem, and improve its generalization property. We provide, in this paper, an numerical study on our proposal to show its effectiveness and characteristics as well as the comparison with conventional Weight Decay technique, which is one of the most common technique for generalization.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Neural Network / L_1 Regression / Robust Statistics / Learning with Hints / Quantile Regression / Generalization Performance / Over Training
Paper # NC2002-92
Date of Issue

Conference Information
Committee NC
Conference Date 2002/12/6(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Proposal of The Neural Network Using Hints on Conditional Quantiles to Improve Its Generalization Performance
Sub Title (in English)
Keyword(1) Neural Network
Keyword(2) L_1 Regression
Keyword(3) Robust Statistics
Keyword(4) Learning with Hints
Keyword(5) Quantile Regression
Keyword(6) Generalization Performance
Keyword(7) Over Training
1st Author's Name Noriyuki YAMANAKA
1st Author's Affiliation Faculty of Engineering, Mie University()
2nd Author's Name Ichiro TAKEUCHI
2nd Author's Affiliation Faculty of Engineering, Mie University
3rd Author's Name Takeshi FURUHASHI
3rd Author's Affiliation Faculty of Engineering, Mie University
Date 2002/12/6
Paper # NC2002-92
Volume (vol) vol.102
Number (no) 508
Page pp.pp.-
#Pages 6
Date of Issue