灵敏度正则化极限学习机及其在数字识别中的应用

Sensitivity Regularized Extreme Learning Machine and Its Application in Digit Recognition

针对正则化极限学习机(RELM)中隐节点数影响分类准确性问题,提出一种灵敏度正则化极限学习机(SRELM)算法.首先根据隐含层激活函数的输出及其相对应的输出层权重系数,推导实际值与隐节点输出值残差相对于隐节点的灵敏度计算公式,然后根据不同隐节点的灵敏度进行排序,利用优化样本的分类准确率删减次要隐节点,从而有效提高SRELM的分类准确率.MNIST手写体数字库实验结果表明,相比于传统的SVM和RELM,SRELM方法的耗时与RELM相差不大,均明显低于SVM,SRELM对手写数字的识别准确率最高.

To solve the problem that the number of hidden nodes in regularized extreme learning machine（RELM） affects classification accuracy, sensitive regularized extreme learning machine（SRELM） algorithm is proposed. Firstly, based on the output of hidden layer activation function and its corresponding output layer weighting factor, the formula of computing the sensitivity for hidden node is deduced by residual between actual value and hidden nodes output. Then different hidden nodes are sorted according to sensitivity. And minor hidden nodes are deleted based on classification accuracy of optimization samples. As a result, SRELM classification accuracy is increased effectively. A case study of MNIST handwritten digit database shows that, compared with common SVM and RELM, time consuming of SRELM is almost the same as RELM, and is obviously lower than SVM. Meanwhile SRELM recognition accuracy for handwritten digit is the highest.