Sparse online greedy support vector regression

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Sparse online greedy support vector regression (2002)

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by Yaakov Engel , Shie Mannor , Ron Meir

Venue:	13th European Conference on Machine Learning
Citations:	45 - 8 self

BibTeX

@INPROCEEDINGS{Engel02sparseonline,
    author = {Yaakov Engel and Shie Mannor and Ron Meir},
    title = {Sparse online greedy support vector regression},
    booktitle = {13th European Conference on Machine Learning},
    year = {2002},
    pages = {84--96}
}

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Abstract

Abstract. We present a novel algorithm for sparse online greedy kernelbased nonlinear regression. This algorithm improves current approaches to kernel-based regression in two aspects. First, it operates online- at each time step it observes a single new input sample, performs an update and discards it. Second, the solution maintained is extremely sparse. This is achieved by an explicit greedy sparsification process that admits into the kernel representation a new input sample only if its feature space image is linearly independent of the images of previously admitted samples. We show that the algorithm implements a form of gradient ascent and demonstrate its scaling and noise tolerance properties on two benchmark regression problems.

Keyphrases

feature space image explicit greedy sparsification process nonlinear regression benchmark regression problem time step sparse online greedy new input sample single new input sample current approach kernel representation kernel-based regression novel algorithm noise tolerance property gradient ascent

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