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Many object recognition or concept identification tasks require accurate detection of large number of classes. These applications present enormous challenges to traditional classification methods, which are mostly designed for solving problems with small number of classes. In this paper, we develop a method called recursive non-negative matrix factorization (RNMF) for building a hierarchical label...
This paper presents a framework called localized support vector machine (LSVM) for classifying data with nonlinear decision surfaces. Instead of building a sophisticated global model from the training data, LSVM constructs multiple linear SVMs, each of which is designed to accurately classify a given test example. A major limitation of this framework is its high computational cost since a unique model...
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