期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 5, 页码 1229-1241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2434841
关键词
Extreme learning machine (ELM); inverse-free; neural networks; optimal updates
类别
资金
- National Natural Science Foundation of China [61401385, 61373086, 61202347, 61375047]
- Young Scientist Foundation of Chongqing [cstc2014kjrc-qnrc40005]
The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.
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