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• W2289161862 abstract "A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a hybrid classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply." @default.
• W2289161862 created "2016-06-24" @default.
• W2289161862 creator A5048495894 @default.
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• W2289161862 date "2016-04-01" @default.
• W2289161862 modified "2023-09-24" @default.
• W2289161862 title "Recognition Using Hybrid Classifiers" @default.
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• W2289161862 doi "https://doi.org/10.1109/tpami.2015.2465910" @default.
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• W2289161862 hasPublicationYear "2016" @default.
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