A Sparse Representation for Function Approximation-Reference-Cited by-同舟云学术

A Sparse Representation for Function Approximation

Published:1998-08-01 Issue:6 Volume:10 Page:1445-1454
ISSN:0899-7667
Container-title:Neural Computation
language:en
Short-container-title:Neural Computation

Author:

Poggio Tomaso¹,Girosi Federico¹

Affiliation:

1. Center for Biological and Computational Learning and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

Abstract

We derive a new general representation for a function as a linear combination of local correlation kernels at optimal sparse locations (and scales) and characterize its relation to principal component analysis, regularization, sparsity principles, and support vector machines.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Link

https://www.mitpressjournals.org/doi/pdf/10.1162/089976698300017250

Reference7 articles.

1. Support-vector networks

2. An Equivalence Between Sparse Approximation and Support Vector Machines

3. Regularization Theory and Neural Networks Architectures

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