Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

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 …

Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

Computational intelligence techniques have been used in wide applications. Out of
numerous computational intelligence techniques, neural networks and support vector …

Human-nameable visual “attributes” can benefit various recognition tasks. However, existing
techniques restrict these properties to categorical labels (for example, a person issmiling'or …

This paper describes a memory-based network that provides estimates of continuous
variables and converges to the underlying (linear or nonlinear) regression surface. This …

This paper proposes a new algorithm for training support vector machines: Sequential
Minimal Optimization, or SMO. Training a support vector machine requires the solution of a …

Online learning from a signal processing perspective There is increased interest in kernel
learning algorithms in neural networks and a growing need for nonlinear adaptive …

According to conventional neural network theories, single-hidden-layer feedforward
networks (SLFNs) with additive or radial basis function (RBF) hidden nodes are universal …

In this paper, we develop an online sequential learning algorithm for single hidden layer
feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a …