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dc.contributor.authorAthitsos, Vassilisen_US
dc.contributor.authorSclaroff, Stanen_US
dc.date.accessioned2011-10-20T04:19:14Z
dc.date.available2011-10-20T04:19:14Z
dc.date.issued2004-02-13
dc.identifier.urihttps://hdl.handle.net/2144/1534
dc.description.abstractThis paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-nearest neighbor classification. Given a family of distance measures as input, AdaBoost is used to learn a weighted distance measure, that is a linear combination of the input measures. The proposed method can be seen both as a novel way to learn a distance measure from data, and as a novel way to apply boosting to multiclass recognition problems, that does not require output codes. In our approach, multiclass recognition of objects is reduced into a single binary recognition task, defined on triples of objects. Preliminary experiments with eight UCI datasets yield no clear winner among our method, boosting using output codes, and k-nn classification using an unoptimized distance measure. Our algorithm did achieve lower error rates in some of the datasets, which indicates that, in some domains, it may lead to better results than existing methods.en_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-2004-006
dc.titleBoosting Nearest Neighbor Classifiers for Multiclass Recognitionen_US
dc.typeTechnical Reporten_US


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