Large Database Recognition Tasks: A Proposal for Partitioning the Data Matrix Required to Train a Radial Basis Functions Networks

Authors:
Rossella Cancelliere
Affiliations:
-
Venue:
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
Year:
1996

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Cited 1

A High Parallel Procedure to Initialize the Output Weights of a Radial Basis Function or BP Neural Network

PARA '00 Proceedings of the 5th International Workshop on Applied Parallel Computing, New Paradigms for HPC in Industry and Academia

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Abstract

When a Radial Basis Functions Network (RBFN) is used to perform recognition tasks, a matrix is built that contains the projections of the input vectors into the space of RBF; the dimension of this matrix depends on the number of RBF used and on the number of vectors in the training set, i.e. the number of vectors chosen in the input space. In this paper we deal with the problems arising when this number is very large, thus making difficult every operations we want to perform with the matrix; we suggest a technique to paginate the matrices involved in calculations so obtaining our aim in a fast way.