We study the relationship between Support Vector Machines (SVM) and Least Squares SVM (LS-SVM). Our main result shows that under mild conditions, LS-SVM for binaryclass classifications is equivalent to the hard margin SVM based on the well-known Mahalanobis distance measure. We further study the asymptotics of the hard margin SVM when the data dimensionality tends to infinity with a fixed sampl...

forecasting of municipal waste generation is a critical challenge for decision making and planning,because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (msw) streams analysis and accurate predictions of solid waste quantities generated. due to dynamic and complexity of solid waste management system, models by artificial intell...

the support vector machine (svm) is a relatively new machine learning method which is increasingly being applied to engineering problems and have yielded encouraging results. because of complex behavior of elastoplastic of web panels of plate girders under patch loading, almost none of the proposed methods provides consistent and accurate predictions of patch load capacity. consequently, altern...

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...

recently, hardware sensors are widely used in monitoring and measurement of water quality parameters. constraint of the instrument to measure some water quality parameters such as the 5-day biochemical oxygen demand (bod5), which are time consuming, causes efforts are diverted to the use of software sensors for online prediction of bod5. the main goal of this research is developing an appropria...