{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:27:01Z","timestamp":1740461221414,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015]]},"abstract":"<jats:p>Local SVM is a lazy learner combining k-nearest neighbor search and support vector machine classifier. We propose an improved implementation of local SVM which utilizes tree structure for efficient nearest neighbor search and a method to avoid unnecessary SVM training in areas far from decision boundary. The proposed lazy learner has great advantage on cross-validation efficiency while maintaining comparable accuracy to traditional SVM. The proposed method also enables us to conduct leave-one-out cross-validation which is previously considered too time-consuming to be practical on large dataset.<\/jats:p>","DOI":"10.3233\/978-1-61499-484-8-491","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T11:58:24Z","timestamp":1740398304000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Local Support Vector Machine With Fast Cross-Validation Capability"],"prefix":"10.3233","author":[{"family":"Chen Yu-Ann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Chung Pau-Choo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Intelligent Systems and Applications"],"original-title":[],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T12:01:53Z","timestamp":1740398513000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISSNISBN&issn=0922-6389&volume=274&spage=491"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-484-8-491","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2015]]}}}