多隐层输出矩阵极限学习机

得益于隐层节点学习参数的随机选择，极限学习机（extreme learning machine, ELM）在学习速度极快的基础上，可以达到较为良好的分类性能。但是，当隐层节点参数完全随机选择时，ELM的性能并不总能达到最优。本文提出多隐层输出矩阵极限学习机（multiple hidden layer output matrices extreme learning machine, M-ELM）方法解决这一问题，该方法通过对不同输出矩阵加权运算以优化隐层节点结构，其中权系数与输出权值在学习过程中同时分析确定。另外，利用该方法可以实现特征级融合ELM。实验证明，对于真实分类问题，M-ELM可以提供比ELM更为准确的分类结果。

Multiple hidden layer output matrices extreme learning machine

The extreme learning machine (ELM) achieves good performance for classification and runs at a fast learning speed because of choosing the learning parameters of hidden nodes randomly. However, when the parameters of the hidden nodes are absolutely randomly chosen, the performance of ELM is not always optimal. The multiple hidden layer output matrices extreme learning machine (M-ELM) is proposed which optimizes the architecture of hidden nodes by weighted calculation of different output matrices, and the matrices weights and the output weights are analytically determined simultaneously. In addition, the feature level fusion of ELM can be achieved by this method. For the real word classification problems, simulation experiments verify that M-ELM can provide a better performance than ELM.