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• W2161366100 abstract "The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly non-linear manifold on which most of the data lies. We propose a new non-parametric kernel density estimation method which captures the local structure of an underlying manifold through the leading eigenvectors of regularized local covariance matrices. Experiments in density estimation show significant improvements with respect to Parzen density estimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to SVMs and much superior to the Parzen classifier." @default.
• W2161366100 created "2016-06-24" @default.
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• W2161366100 date "2002-01-01" @default.
• W2161366100 modified "2023-09-29" @default.
• W2161366100 title "Manifold Parzen Windows" @default.
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