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A New Learning Algorithm Based on Lever Principle.

Authors :
Wang, Lipo
Chen, Ke
Ong, Yew
He, Xiaoguang
Tian, Jie
Yang, Xin
Source :
Advances in Natural Computation (9783540283232); 2005, p187-198, 12p
Publication Year :
2005

Abstract

In this paper a new learning algorithm, Lever Training Machine (LTM), is presented for binary classification. LTM is a supervised learning algorithm and its main idea is inspired from a physics principle: Lever Principle. Figuratively, LTM involves rolling a hyper-plane around the convex hull of the target training set, and using the equilibrium position of the hyper-plane to define a decision surfaces. In theory, the optimal goal of LTM is to maximize the correct rejection rate. If the distribution of target set is convex, a set of such decision surfaces can be trained for exact discrimination without false alarm. Two mathematic experiments and the practical application of face detection confirm that LTM is an effective learning algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540283232
Database :
Supplemental Index
Journal :
Advances in Natural Computation (9783540283232)
Publication Type :
Book
Accession number :
32961874
Full Text :
https://doi.org/10.1007/11539087_21