An Effective Machine Learning Algorithm using Momentum Scheduling

H
HIS
2004
Fourth International Conference on Hybrid Intelligent Systems (HIS'04)
Abstract - An Effective Machine Learning Algorithm using Momentum Scheduling

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Fourth International Conference on Hybrid Intelligent Systems (HIS'04)

An Effective Machine Learning Algorithm using Momentum Scheduling (PDF)

Kitakyushu, Japan

December 05-December 08

ISBN: 0-7695-2291-2

	ASCII Text	x
	Eun-Mi Kim, Seong-Mi Park, Kwang-Hee Kim, Bae-Ho Lee, "An Effective Machine Learning Algorithm using Momentum Scheduling," Hybrid Intelligent Systems, International Conference on, pp. 442-443, Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004.

	BibTex	x
	@article{ 10.1109/ICHIS.2004.18, author = {Eun-Mi Kim and Seong-Mi Park and Kwang-Hee Kim and Bae-Ho Lee}, title = {An Effective Machine Learning Algorithm using Momentum Scheduling}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2291-2}, pages = {442-443}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2004.18}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Hybrid Intelligent Systems, International Conference on TI - An Effective Machine Learning Algorithm using Momentum Scheduling SN - 0-7695-2291-2 SP442 EP443 A1 - Eun-Mi Kim, A1 - Seong-Mi Park, A1 - Kwang-Hee Kim, A1 - Bae-Ho Lee, PY - 2004 KW - null VL - 0 JA - Hybrid Intelligent Systems, International Conference on ER -

Eun-Mi Kim, Chonnam National University, Republic of Korea

Seong-Mi Park, Chonnam National University, Republic of Korea

Kwang-Hee Kim, Chonnam National University, Republic of Korea

Bae-Ho Lee, Chonnam National University, Republic of Korea

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2004.18

This paper proposes a new algorithm to improve learning performance in support vector machine by using the Kernel Relaxation and the dynamic momentum. Compared with the static momentum, the dynamic momentum is simultaneously obtained by the learning process of pattern weight and reflected into different momentum by the current state. Therefore, the proposed dynamic momentum algorithm can effectively control the convergence rate and performance. The experiment using SONAR data shows that the proposed algorithm has better convergence rate and performance than the kernel relaxation using static momentum.

Citation:

Eun-Mi Kim, Seong-Mi Park, Kwang-Hee Kim, Bae-Ho Lee, "An Effective Machine Learning Algorithm using Momentum Scheduling," his, pp.442-443, Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004

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