This paper proposes a least-squares temporal difference (LSTD) algorithm based on extreme learning machine that uses a singlehidden layer feedforward network to approximate the value function. While LSTD is typically combined with local function approximators, the proposed approach uses a global approximator that allows better scalability properties. The results of the experiments carried out o...

Web services which are language and platform independent self-contained web-based distributed application components represented by their interfaces can have different Quality of Service (QoS) characteristics such as performance, reliability and scalability. One of the major objectives of a web service provider and implementer is to be able to estimate and improve the QoS parameters of their we...

In recent years, semi-supervised learning has been a hot research topic in machine learning area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each example is represented by two or more r...

Extreme Support Vector Machine (ESVM) is a nonlinear robust SVM algorithm based on regularized least squares optimization for binary-class classification. In this paper, a novel algorithm for regression tasks, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Moreover, kernel ESVR is suggested as well. Experiments show that, ESVR has a better generalization than some other tr...

Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (R...

Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN). To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN) with radial basis function (R...

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this pa...

Application of intelligent methods in industry become a very challenging issue nowadays and will be of extreme importance in the future. Intelligent methods include, fuzzy sets neural networks genetics algorithms and others techniques known as soft computing. No doubt rough set theory can also contribute essentially to this domain. In this paper basic ideas of rough set theory are presented and...