کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
531677 869865 2008 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Generative models for similarity-based classification
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Generative models for similarity-based classification
چکیده انگلیسی

A maximum-entropy approach to generative similarity-based classifiers model is proposed. First, a descriptive set of similarity statistics is assumed to be sufficient for classification. Then the class-conditional distributions of these descriptive statistics are estimated as the maximum-entropy distributions subject to empirical moment constraints. The resulting exponential class-conditional distributions are used in a maximum a posteriori decision rule, forming the similarity discriminant analysis (SDA) classifier. Simulated and real data experiments compare performance to the k-nearest neighbor classifier, the nearest-centroid classifier, and the potential support vector machine (PSVM).

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 41, Issue 7, July 2008, Pages 2289–2297
نویسندگان
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