LAUSR

Extreme Learning Machine (ELM) is a learning algorithm based on generalized single-hidden-layer feed-forward neural network. Since ELM has an excellent performance on regression and classification problems, it has been paid more and more attention recently. The determination of structure of ELM plays a vital role in ELM applications. Essentially, determination of the structure of ELM is equivalent to the determination of the hidden layer structure. Utilizing a smaller scale of the hidden layer structure can promote faster running speed. In this paper, we propose algorithm PCI-ELM (Pruned-Convex Incremental Extreme Learning Machine) based on CI-ELM (Convex Incremental Extreme Learning Machine). Furthermore, we also present an improved PCI-ELM algorithm, EPCI-ELM (Enhanced Pruned-Convex Incremental Extreme Learning Machine), which introduces a filtering strategy for PCI-ELM during the neurons adding process. In order to adjust the single-hidden-layer feed-forward neural network more flexibly and achieve the most compact form of the hidden layer structure, in this paper, we propose a algorithm which can dynamically determine hidden layer structure, DCI-ELM (Dynamic Convex Incremental Extreme Learning Machine). At the end of this paper, we verify the performance of PCI-ELM, EPCI-ELM and DCI-ELM. The results show that PCI-ELM, EPCI-ELM and DCI-ELM control hidden layer structure very well and construct the more compact single-hidden-layer feed-forward neural network.