Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification
提出一种基于极限学习机的方法，通过将残差网络误差拉回隐藏层来生长子网络隐藏节点，在显著减少节点数的同时保持或提升泛化性能，学习速度比传统ELM、反向传播和支持向量机快数百倍。
As demonstrated earlier, the learning effectiveness and learning speed of single-hidden-layer feedforward neural networks are in general far slower than required, which has been a major bottleneck for many applications. Huang et al. proposed extreme learning machine (ELM) which improves the training speed by hundreds of times as compared to its predecessor learning techniques. This paper offers an ELM-based learning method that can grow subnetwork hidden nodes by pulling back residual network error to the hidden layer. Furthermore, the proposed method provides a similar or better generalization performance with remarkably fewer hidden nodes as compared to other ELM methods employing huge number of hidden nodes. Thus, the learning speed of the proposed technique is hundred times faster compared to other ELMs as well as to back propagation and support vector machines. The experimental validations for all methods are carried out on 32 data sets.