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Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering

Author(s)
Poggio, Tomaso; Girosi, Federico
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Abstract
The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of three-layer networks that we call regularization networks or Hyper Basis Functions. These networks are also closely related to the classical Radial Basis Functions used for interpolation tasks and to several pattern recognition and neural network algorithms. In this note, we extend the theory by defining a general form of these networks with two sets of modifiable parameters in addition to the coefficients $c_\\ alpha$: moving centers and adjustable norm- weight.
Date issued
1990-04-01
URI
http://hdl.handle.net/1721.1/6014
Other identifiers
AIM-1167
Series/Report no.
AIM-1167
Keywords
learning networks, regularization

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