Support vector method for multivariate density estimation (2000) [20 citations — 0 self]
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by Vladimir N. Vapnik, Sayan Mukherjee
Advances in Neural Information Processing Systems
http://www.ai.mit.edu/people/sayan/webPub/nips.ps.Z
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Abstract:
A new method for multivariate density estimation is developed based on the Support Vector Method (SVM) solution of inverse ill-posed problems. The solution has the form of a mixture of densities. This method with Gaussian kernels compared favorably to both Parzen's method and the Gaussian Mixture Model method. For synthetic data we achieve more accurate estimates for densities of 2, 6, 12, and 40 dimensions. 1
Citations
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