Extreme learning machine (ELM) has been proved to be an effective pattern classification and regression mechanism by researchers. However, its good performance is based on a large number of hidden layer nodes. With the increase nodes in layers, computation cost greatly increased. In this paper, we propose novel algorithm, named constrained voting extreme (CV-ELM). Compared with traditional ELM,...

Generalized Extreme Learning Machine (GELM) is a kind of fast and efficient learning algorithm for training Generalized Single-layer hidden Feedforward Networks (GSLFNs) acting on some metric spaces. However, noisy data often produce over-fitting phenomena in practical applications. Therefore, an improved learning algorithm, called Regularized Generalized Extreme Learning Machine (R-GELM), is p...

In this paper, a new algorithm for function approximation is proposed to obtain better generalization performance and faster convergent rate. The new algorithm incorporates the architectural constraints from a priori information of the function approximation problem into Extreme Learning Machine. On one hand, according to Taylor theorem, the activation functions of the hidden neurons in this al...

Due to its importance in many applications, the incomplete data mining has received increasing attention in recent years, but there has been little study of the cost-sensitive classification on incomplete data. Therefore this paper proposes the dynamic costsensitive extreme learning machine for classification of incomplete data based on the deep imputation network (DCELMIDC). Firstly, we propos...