@inproceedings{fe62b539e69a487489a257dcdadf3416,
title = "Forward and backward selection in regression hybrid network",
abstract = "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.",
keywords = "Clustering, Hybrid network architecture, Model selection, Nested models, Regularization, SMLP",
author = "Shimon Cohen and Nathan Intrator",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 3rd International Workshop on Multiple Classifier Systems, MCS 2002 ; Conference date: 24-06-2002 Through 26-06-2002",
year = "2002",
doi = "10.1007/3-540-45428-4_10",
language = "אנגלית",
isbn = "3540438181",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "98--107",
editor = "Fabio Roli and Josef Kittler",
booktitle = "Multiple Classifier Systems - 3rd International Workshop, MCS 2002, Proceedings",
}