{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T19:09:52Z","timestamp":1648926592176},"reference-count":37,"publisher":"World Scientific Pub Co Pte Lt","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:p> The nearest neighbor method is one of the most widely used pattern classification methods. However its major drawback in practice is the curse of dimensionality. In this paper, we propose a new method to alleviate this problem significantly. In this method, we attempt to cover the training patterns of each class with a number of hyperspheres. The method attempts to design hyperspheres as compact as possible, and we pose this as a quadratic optimization problem. We performed several simulation experiments, and found that the proposed approach results in considerable speed-up over the k-nearest-neighbor method while maintaining the same level of accuray. It also significantly beats other prototype classification methods (Like LVQ, RCE and CCCD) in most performance aspects. <\/jats:p>","DOI":"10.1142\/s0218001409007740","type":"journal-article","created":{"date-parts":[[2009,12,30]],"date-time":"2009-12-30T11:43:21Z","timestamp":1262173401000},"page":"1549-1575","source":"Crossref","is-referenced-by-count":1,"title":["HYPERSPHERICAL PROTOTYPES FOR PATTERN CLASSIFICATION"],"prefix":"10.1142","volume":"23","author":[{"given":"HATEM A.","family":"FAYED","sequence":"first","affiliation":[{"name":"Department of Engineering Mathematics and Physics, Cairo University, Giza, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"AMIR F.","family":"ATIYA","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Cairo University, Giza, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"SHERIF M. 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