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MML INFERENCE OF SINGLELAYER NEURAL NETWORKS ENES MAKALIC
 

Summary: MML INFERENCE OF SINGLE­LAYER NEURAL NETWORKS
ENES MAKALIC
School of Computer Science and Software Engineering
Monash University
Clayton, VIC 3800, Australia
email: enesm@csse.monash.edu.au
LLOYD ALLISON AND DAVID L. DOWE
School of Computer Science and Software Engineering
Monash University
Clayton, VIC 3800, Australia
email: lloyd@csse.monash.edu.au
ABSTRACT
The architecture selection problem is of great importance
when designing neural networks. A network that is too
simple does not learn the problem sufficiently well. Con­
versely, a larger than necessary network presumably in­
dicates overfitting and provides low generalisation perfor­
mance. This paper presents a novel architecture selection
criterion for single hidden layer feedforward networks. The
optimal network size is determined using a version of the

  

Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University

 

Collections: Computer Technologies and Information Sciences