Conventional extreme learning machines (ELMs) solve a Moore–Penrose generalized
inverse of hidden layer activated matrix and analytically determine the output weights to …

Extreme learning machine (ELM) is a novel and recent machine learning algorithm which
was first proposed by Huang et al.(Proceedings of 2004 IEEE international joint conference …

In this paper, a new hidden layer construction method for Extreme Learning Machines
(ELMs) is investigated, aimed at generating a diverse set of weights. The paper proposes …

An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural
network (FNN) learning system that randomly assigns the connections with and within …

This paper proposes a learning algorithm called Semi-supervised Online Sequential ELM,
denoted as SOS-ELM. It aims to provide a solution for streaming data applications by …

Extreme learning machine (ELM) has gained increasing interest from various research fields
recently. In this review, we aim to report the current state of the theoretical research and …

An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning
system whose connections with output neurons are adjustable, while the connections with …

Monotonic classification problems mean that both feature values and class labels are
ordered and monotonicity relationships exist between some features and the decision label …

Extreme learning machines are randomly initialized single-hidden layer feed-forward neural
networks where the training is restricted to the output weights in order to achieve fast …

Extreme learning machine (ELM) was proposed as a new class of learning algorithm for
single-hidden layer feedforward neural network (SLFN) much faster than the traditional …