this paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. in this method, mammograms are segmented into regions of interest (roi) in order to extract features including geometrical and contourlet coefficients. the extracted features benefit from...

In this paper, two classifiers are proposed to distinguish between bulking and nonbulking situations in an activated sludge wastewater treatment plant, based on available image analysis information. The first classifier consists of a simple linear classification function, while the second classifier uses a highly nonlinear least squares support vector machine (LS-SVM) to distinguish between bot...

Weighted least squares support vector machine (WLSSVM) is a robust version of least squares support vector machine (LS-SVM). It adds weights on error variables to eliminate the influence of outliers. But the weights, which largely depend on the original regression errors from unweighted LS-SVM, might be unreliable for correcting the biased estimation of LS-SVM, especially for the training data ...

This study presents a least squares support vector machine (LSSVM) based displacement prediction model for health monitoring of concrete dams. LSSVM is a novel machine learning technique. The model can produce similar good generalization performance and learns faster than the basic support vector machines in engineering problems. The advantages such as high prediction accuracy, fast training sp...

Support Vector Machines (SVMs) have become a popular classification tool. Because of their theoretical robustness they offer improvements in pattern classification applications. This paper describes an approach of producing a N-best list of hypotheses for the needs of phoneme recognition, using a Least Squares Support Vector Machine classifier (LS-SVM) and generate the corresponding N-best list...

In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equalit y constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. Ho wever, a d r a wback is that sparseness is lost in the LS-SVM ...

In least squares support vector machine (LS-SVM) classi-ers the original SVM formulation of Vapnik is modiied by considering equality constraints within a form of ridge regression instead of inequality constraints. As a result the solution follows from solving a set of linear equations instead of a quadratic programming problem. However, a drawback is that sparseness is lost in the LS-SVM case ...