Smooth augmented Lagrangian method for twin bounded support vector machine

Abstract

<p style='text-indent:20px;'>In this paper, we propose a method for solving the twin bounded support vector machine (TBSVM) for the binary classification. To do so, we use the augmented Lagrangian (AL) optimization method and smoothing technique, to obtain new unconstrained smooth minimization problems for TBSVM classifiers. At first, the augmented Lagrangian method is recruited to convert TBSVM into unconstrained minimization programming problems called as AL-TBSVM. We attempt to solve the primal programming problems of AL-TBSVM by converting them into smooth unconstrained minimization problems. Then, the smooth reformulations of AL-TBSVM, which we called AL-STBSVM, are solved by the well-known Newton's algorithm. Finally, experimental results on artificial and several University of California Irvine (UCI) benchmark data sets are provided along with the statistical analysis to show the superior performance of our method in terms of classification accuracy and learning speed.</p>

Crop Disease and Pest Identification Technology Based on Acpso-SVM Algorithm Optimization

“…[ , , , , , , , , ] In which, a method combining gray level co-occurrence matrix (GLCM) with local binary pattern (LBP) is put forward (Bazikar et al, 2022;Rehman et al, 2022). GLCM is a second-order combined conditional probability density function of an image, which describes the relative frequency of different gray level pixels appearing again in the window.…”