Density estimation using support vector machines (original) (raw)

1997

Abstract

In this report we describe how the Support Vector (SV) technique of solving linear operator equations can be applied to the problem of density estimation 4]. We present a new optimization procedure and set of kernels closely related to current SV techniques that guarantee the monotonicity of the approximation. This technique estimates densities with a mixture of bumps (Gaussian-like shapes), with the usual SV property that only some coe cients are non-zero. Both the width and the height of each bump is chosen adaptively, by ...

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