An Effective Machine Learning Algorithm using Momentum Scheduling

Authors:
Eun-Mi Kim;Seong-Mi Park;Kwang-Hee Kim;Bae-Ho Lee
Affiliations:
Chonnam National University, Republic of Korea;Chonnam National University, Republic of Korea;Chonnam National University, Republic of Korea;Chonnam National University, Republic of Korea
Venue:
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Year:
2004

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Abstract

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.