Abstract
A component neural networks parallel training algorithm PLA is proposed, which encourages component neural network to learn from expected goal and the others, so all component neural networks are trained simultaneously and interactively. In the stage of combining component neural networks, we provide a parallel weight optimal approach GASEN-e by expanding GASEN proposed by Zhou et al, which assign weight for every component neural network and bias for their ensemble. Experiment results show that a neural networks ensemble system is efficient constructed by PLA and GASEN-e.
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Wang, ZQ., Chen, SF., Chen, ZQ., Xie, JY. (2004). A Parallel Learning Approach for Neural Network Ensemble. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_123
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DOI: https://doi.org/10.1007/978-3-540-30549-1_123
Publisher Name: Springer, Berlin, Heidelberg
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