The Support Vector Machine (SVM) has been successfully applied for classification problems in many different fields. It was originally proposed using the idea of searching for the maximum separation hyperplane. In this article, in contrast to the criterion of maximum separation, we explore alternative searching criteria which result in the new method, the Bounded Constraint Machine (BCM). Prope...

Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality ...

Laser ultrasonic defect detection and classification has been widely used in engineering and material defect detection, so detecting and classifying the defect targets accurately is significant. In order to obtain the higher classification accuracy, an improved support vector machine (SVM) based on particle swarm optimization algorithm is used as classifier in this paper. To search the optimal ...

Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decod...

a quantitative structure–activity relationship (qsar) study is suggested for the prediction of biological activity (pic 50 ) of 3, 4-dihydropyrido [3 ,2-d] pyrimidone derivatives as p38 inhibitors. modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (pca) and least square support vector mac...

In this paper we present improved training algorithms to two newly developed classifiers, reduced set vector machines and Adaboost cascade classifier applied in face detection, which are all based on learning from data. Support vector machine (SVM) has been proved to be a powerful tool for solving practical pattern recognition problems based on learning from data. Due to large number of support...

Because applying machine learning techniques in support of clinical decision would improve decision makers in healthcare, we present in this paper a comparative framework of Support Vector Machine (SVM) classifiers based on post operative patient (POP) data. We compare the performance of a single multiclass SVM and a multistage SVM (MSVM) to those obtained by a number of other classifiers prese...

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