关键词: 分类, 极限学习机, 泛化能力, 隐层优化

Abstract: On the classification of big data background, this paper proposes a fast hidden layer optimal strategy to solve the prominent problem in the training process of Extreme Learning Machine（ELM）, which needs a single ELM to be trained by too many iterations to optimize the number of hidden layer nodes or better generalization performance of the model. Without additional time complexity, the proposed algorithm trains the hidden layer networks parallelly and simultaneously, i.e., the generalization ability of the model and the number of hidden layer nodes are optimized thoughtfully, as well, it avoids a large number of repeated calculations by distributed calculation. Meanwhile, the proposed algorithm can learn the data via more accurate, more intuitive comparison of different influences due to the hidden nodes in various number. Based on the experimental results on many types of standard tests, compared with the traditional distributed ELM, the proposed algorithm greatly improves the performance in solving accuracy, generalized ability and stability.

Key words: classification, extreme learning machine, generalization performance, hidden layer optimal