Forward and backward selection in regression hybrid network

Shimon Cohen, Nathan Intrator

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We introduce a Forward Backward and Model Selection algorithm (FBMS) for constructing a hybrid regression network of radial and perceptron hidden units. The algorithm determines whether a radial or a perceptron unit is required at a given region of input space. Given an error target, the algorithm also determines the number of hidden units. Then the algorithm uses model selection criteria and prunes unnecessary weights. This results in a final architecture which is often much smaller than a RBF network or a MLP. Results for various data sizes on the Pumadyn data indicate that the resulting architecture competes and often outperform best known results for this data set.

Original languageEnglish
Title of host publicationMultiple Classifier Systems - 3rd International Workshop, MCS 2002, Proceedings
EditorsFabio Roli, Josef Kittler
PublisherSpringer Verlag
Number of pages10
ISBN (Print)3540438181, 9783540438182
StatePublished - 2002
Event3rd International Workshop on Multiple Classifier Systems, MCS 2002 - Cagliari, Italy
Duration: 24 Jun 200226 Jun 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Workshop on Multiple Classifier Systems, MCS 2002


  • Clustering
  • Hybrid network architecture
  • Model selection
  • Nested models
  • Regularization
  • SMLP


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