A new learning algorithm based on lever principle

Authors:
Xiaoguang He;Jie Tian;Xin Yang
Affiliations:
Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science,Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy ...;Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science,Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy ...;Center for Biometrics and Security Research, Key Laboratory of Complex Systems and Intelligence Science,Institute of Automation, Chinese Academy of Sciences, Graduate School of the Chinese Academy ...
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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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.