Parzen windows for multi-class classification
نویسندگان
چکیده
منابع مشابه
Manifold Parzen Windows
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 est...
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To escape from the curse of dimensionality, we claim that one can learn non-local functions, in the sense that the value and shape of the learned function at x must be inferred using examples that may be far from x. With this objective, we present a non-local non-parametric density estimator. It builds upon previously proposed Gaussian mixture models with regularized covariance matrices to take...
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This paper is concerned with image registration, i.e. establishing correspondence between objects in different images undergoing geometric and photometric transformations in the presence of occlusions and clutter. Challenges to accurate registration comes from three factors presence of background clutter, occlusion of the pattern being registered and change in feature values across images. We a...
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ژورنال
عنوان ژورنال: Journal of Complexity
سال: 2008
ISSN: 0885-064X
DOI: 10.1016/j.jco.2008.07.001