{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T19:54:49Z","timestamp":1760385289408},"reference-count":19,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2013,5]]},"abstract":"<jats:p> Sparse LS-SVM yields better generalization capability and reduces prediction time in comparison to full dense LS-SVM. However, both methods require careful selection of hyper-parameters (HPS) to achieve high generalization capability. Leave-One-Out Cross Validation (LOO-CV) and k-fold Cross Validation (k-CV) are the two most widely used hyper-parameter selection methods for LS-SVMs. However, both fail to select good hyper-parameters for sparse LS-SVM. In this paper we propose a new hyper-parameter selection method, LGEM-HPS, for LS-SVM via minimization of the Localized Generalization Error (L-GEM). The L-GEM consists of two major components: empirical mean square error and sensitivity measure. A new sensitivity measure is derived for LS-SVM to enable the LGEM-HPS select hyper-parameters yielding LS-SVM with smaller training error and minimum sensitivity to minor changes in inputs. Experiments on eleven UCI data sets show the effectiveness of the proposed method for selecting hyper-parameters for sparse LS-SVM. <\/jats:p>","DOI":"10.1142\/s0219691313500306","type":"journal-article","created":{"date-parts":[[2013,6,5]],"date-time":"2013-06-05T06:59:38Z","timestamp":1370415578000},"page":"1350030","source":"Crossref","is-referenced-by-count":15,"title":["HYPER-PARAMETER SELECTION FOR SPARSE LS-SVM VIA MINIMIZATION OF ITS LOCALIZED GENERALIZATION ERROR"],"prefix":"10.1142","volume":"11","author":[{"given":"BINBIN","family":"SUN","sequence":"first","affiliation":[{"name":"Department of Computer Science, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, P. R. China"}]},{"given":"WING W. Y.","family":"NG","sequence":"additional","affiliation":[{"name":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006 Guangzhou, P. R. China"}]},{"given":"DANIEL S.","family":"YEUNG","sequence":"additional","affiliation":[{"name":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006 Guangzhou, P. R. China"}]},{"given":"PATRICK P. K.","family":"CHAN","sequence":"additional","affiliation":[{"name":"Machine Learning and Cybernetics Research Center, School of Computer Science and Engineering, South China University of Technology, 510006 Guangzhou, P. R. 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