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An efficient gait recognition based on a selective neural network ensemble
Author(s) -
Lee Heesung,
Hong Sungjun,
Kim Euntai
Publication year - 2008
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20136
Subject(s) - computer science , artificial neural network , generalization , artificial intelligence , ensemble learning , minification , task (project management) , machine learning , time delay neural network , ensemble forecasting , pattern recognition (psychology) , mathematics , mathematical analysis , management , economics , programming language
The neural network ensemble is a learning paradigm where a collection of neural networks is trained for the same task. Generally, the ensemble shows better generalization performance than a single neural network. In this article, a selective neural network ensemble is applied to gait recognition. The proposed method selects some neural network based on the minimization of generalization error. Since the selection rule is directly incorporated into the cost function, we can obtain adequate component networks to constitute an ensemble. Experiments are performed with the NLPR database to show the performance of the proposed algorithm. © 2008 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 18, 237–241, 2008; Published online in Wiley InterScience (www.interscience.wiley.com).