منابع مشابه
Mixtures of Large Margin Nearest Neighbor Classifiers
The accuracy of the k-nearest neighbor algorithm depends on the distance function used to measure similarity between instances. Methods have been proposed in the literature to learn a good distance function from a labelled training set. One such method is the large margin nearest neighbor classifier that learns a global Mahalanobis distance. We propose a mixture of such classifiers where a gati...
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The nearest neighbor classification is a simple and yet effective technique for pattern recognition. Performance of this technique depends significantly on the distance function used to compute similarity between examples. Some techniques were developed to learn weights of features for changing the distance structure of samples in nearest neighbor classification. In this paper, we propose an ap...
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Large margin nearest neighbor classification (LMNN) is a popular technique to learn a metric that improves the accuracy of a simple knearest neighbor classifier via a convex optimization scheme. However, the optimization problem is convex only under the assumption that the nearest neighbors within classes remain constant. In this contribution we show that an iterated LMNN scheme (multi-pass LMN...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 2005
ISSN: 1045-9227
DOI: 10.1109/tnn.2005.849821