Pruning Using Parameter and Neuronal Metrics

[Pierre v.d. Laar]

Dear Connectionists,

The following article which has been accepted for publication in
Neural Computation can now be downloaded from our ftp-server as
ftp://ftp.mbfys.kun.nl/snn/pub/reports/vandeLaar.NC98.ps.Z

	Yours sincerely,
			Pierre van de Laar


Pruning Using Parameter and Neuronal Metrics
written by Pierre van de Laar and Tom Heskes

Abstract: 

In this article, we introduce a measure of optimality for architecture
selection algorithms for neural networks: the distance from the
original network to the new network in a metric that is defined by the
probability distributions of all possible networks. We derive two
pruning algorithms, one based on a metric in parameter space and
another one based on a metric in neuron space, which are closely
related to well-known architecture selection algorithms, such as
GOBS. Furthermore, our framework extends the theoretically range of
validity of GOBS and therefore can explain results observed in
previous experiments. In addition, we give some computational
improvements for these algorithms.



FTP INSTRUCTIONS

unix% ftp ftp.mbfys.kun.nl
Name: anonymous
Password: (use your e-mail address)
ftp> cd snn/pub/reports/
ftp> binary
ftp> get vandeLaar.NC98.ps.Z
ftp> bye
unix% uncompress vandeLaar.NC98.ps.Z
unix% lpr vandeLaar.NC98.ps