## TR#271: On The Efficiency of The Orthogonal Least Squares Training Method For Radial Basis Function Networks

### Alex Sherstinsky and Rosalind W. Picard

Article available in:
IEEE Transactions on Neural Networks

February, 1994

The efficiency of the Orthogonal Least Squares (OLS) method for training
approximation networks is examined using the criterion of energy compaction.
We show that the selection of basis vectors produced by the procedure is not
the most compact when the approximation is performed using a non-orthogonal
basis. Hence, the algorithm does not produce the smallest possible networks
for a given approximation error. Specific examples are given using the
Gaussian Radial Basis Functions (RBF) type of approximation networks.

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