In this paper, we propose some improvements for the implementations of least squares support vector machine classifiers (LS-SVM). An improved conjugate gradient scheme is proposed for solving the optimization problems in LS-SVM, and an improved SMO algorithm is put forward for the general unconstrained quadratic programming problems which is the case of LS-SVM without the bias term. Numerical e...

A critical issue in urban cellular automata (CA) modeling concerns the identification of transition rules that generate realistic urban land use patterns. Recent studies have demonstrated that linear methods cannot sufficiently delineate the extraordinary complex boundaries between urban and non-urban areas and as most urban CA models simulate transitions across these boundaries, there is an ur...

This paper proposes a Multiclass Least Squares Twin Support Vector Machine (MLSTSVM) classifier for multi-class classification problems. The formulation of MLSTSVM is obtained by extending the formulation of recently proposed binary Least Squares Twin Support Vector Machine (LSTSVM) classifier. For M-class classification problem, the proposed classifier seeks M-non parallel hyper-planes, one fo...

Among Neural Network methods, the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they automatically derive the “optimal” network structure, in respect to generalization error for a given problem. In practice it means, that a lot of decisions that had to be made during the design of a traditional NN (e.g. the number of neurons, the length and type of t...

In this paper, we propose a new feature selection approach for the recently proposed Least Squares Projection Twin Support Vector Machine (LSPTSVM) for binary classification. 1-norm is used in our feature selection objective so that only non-zero elements in weight vectors will be chosen as selected features. Also, the Tikhonov regularization term is incorporated to the objective of our approac...

A scheme of direct adaptive H∞ control based on least squares support vector machines (LS-SVM) is proposed for a class of nonlinear uncertain systems. In this method, LS-SVM is employed to construct the adaptive controller, and an on-line learning rule for the weighting vector and bias is derived. A parameter selection method based on the genetic algorithm (GA) is given for LS-SVM regression wi...

Here we propose a novel machine learning method for time series forecasting which is based on the widelyused Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting as this paper will show. The proposed method is a generalizati...

Support Vector Machine has appeared as an active study in machine learning community and extensively used in various fields including in prediction, pattern recognition and many more. However, the Least Squares Support Vector Machine which is a variant of Support Vector Machine offers better solution strategy. In order to utilize the LSSVM capability in data mining task such as prediction, ther...

This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the n...

This research paper proposes an intelligent classification technique to identify tumor. The manual interpretation of tumor based on visual examination by Radiologist/physician may lead to missing diagnosis when a large number of data are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of medical image after ...