Efficient training of RBF networks for classification

Ian T. Nabney

Efficient training of RBF networks for classification

Ian T. Nabney

Computer Science Research Group

Research output: Unpublished contribution to conference › Unpublished Conference Paper › peer-review

Abstract

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Original language	English
Pages	210-215
Number of pages	6
Publication status	Published - 1999
Event	9th International Conference on Artificial Neural Networks - Edinburgh, United Kingdom Duration: 7 Sept 1999 → 7 Sept 1999

Conference

Conference	9th International Conference on Artificial Neural Networks
Abbreviated title	ICANN 99
Country/Territory	United Kingdom
City	Edinburgh
Period	7/09/99 → 7/09/99

Bibliographical note

Volume 1 ISSN - 0537-9989

Keywords

Radial Basis
regression
Multi-layer Perceptrons
probabilities
logistic
softmax outputs
Generalised Linear Models
non-linear optimisation
datasets

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Artificial_Neural_Networks_7(470).pdf
Volume 1 ISSN - 0537-9989
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Cite this

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title = "Efficient training of RBF networks for classification",

abstract = "Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.",

keywords = "Radial Basis, regression, Multi-layer Perceptrons, probabilities, logistic, softmax outputs, Generalised Linear Models, non-linear optimisation, datasets",

author = "Nabney, {Ian T.}",

note = "Volume 1 ISSN - 0537-9989; 9th International Conference on Artificial Neural Networks, ICANN 99 ; Conference date: 07-09-1999 Through 07-09-1999",

year = "1999",

language = "English",

pages = "210--215",

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AU - Nabney, Ian T.

N1 - Volume 1 ISSN - 0537-9989

PY - 1999

Y1 - 1999

N2 - Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

AB - Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

KW - Radial Basis

KW - regression

KW - Multi-layer Perceptrons

KW - probabilities

KW - logistic

KW - softmax outputs

KW - Generalised Linear Models

KW - non-linear optimisation

KW - datasets

UR - http://www.scopus.com/inward/record.url?scp=0033322387&partnerID=8YFLogxK

UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=819722&isnumber=17760

M3 - Unpublished Conference Paper

SP - 210

EP - 215

T2 - 9th International Conference on Artificial Neural Networks

Y2 - 7 September 1999 through 7 September 1999

ER -

Efficient training of RBF networks for classification

Abstract

Conference

Bibliographical note

Keywords

Access to Document

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Cite this