<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patil, N. S.</style></author><author><style face="normal" font="default" size="100%">Shelokar, P. S.</style></author><author><style face="normal" font="default" size="100%">Jayaraman, Valadi K.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, B. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regression models using pattern search assisted least square support vector machines</style></title><secondary-title><style face="normal" font="default" size="100%">Chemical Engineering Research and Design</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">equality constraints</style></keyword><keyword><style  face="normal" font="default" size="100%">LS-SVM</style></keyword><keyword><style  face="normal" font="default" size="100%">model selection</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">pattern search</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">A8</style></number><publisher><style face="normal" font="default" size="100%">INST CHEMICAL ENGINEERS</style></publisher><pub-location><style face="normal" font="default" size="100%">165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLAND</style></pub-location><volume><style face="normal" font="default" size="100%">83</style></volume><pages><style face="normal" font="default" size="100%">1030-1037</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">2.525</style></custom4></record></records></xml>