article

Generalized radial basis function networks for classification and novelty detetion: self-organization of optional Bayesian decision

Authors:

P. TavanAuthors Info & Claims

Neural Networks, Volume 13, Issue 10

Pages 1075 - 1093

https://doi.org/10.1016/S0893-6080(00)00060-5

Published: 01 December 2000 Publication History

Abstract

No abstract available.

Cited By

View all

Luo SCheng J(2017)VPMCD based novelty detection method on and its application to fault identification for local characteristic-scale decompositionCluster Computing10.1007/s10586-017-0932-220:4(2955-2965)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10586-017-0932-2
Nayak PMishra SDash PBisoi R(2016)Comparison of modified teaching---learning-based optimization and extreme learning machine for classification of multiple power signal disturbancesNeural Computing and Applications10.1007/s00521-015-2010-027:7(2107-2122)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s00521-015-2010-0
Lu JHu HBai Y(2015)Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithmNeurocomputing10.1016/j.neucom.2014.10.065152:C(305-315)Online publication date: 25-Mar-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.10.065
Show More Cited By

Index Terms

Generalized radial basis function networks for classification and novelty detetion: self-organization of optional Bayesian decision
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Semantic networks
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

Recommendations

Classification by evolutionary generalised radial basis functions
Advances in Intelligent Agent Systems

This paper proposes a Neural Network model using Generalised kernel functions for the hidden layer of a feed forward network. These functions are Generalised Radial Basis Functions (GRBF), and the architecture, weights and node topology are learned ...
Classification by Evolutionary Generalized Radial Basis Functions
ISDA '09: Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications

This paper proposes a novelty neural network model by using generalized kernel functions for the hidden layer of a feed forward network (Generalized Radial Basis Functions, GRBF), where the architecture, weights and node typology are learned through an ...
Generalised Gaussian radial basis function neural networks

The mixed use of different shapes of radial basis functions (RBFs) in radial basis functions neural networks (RBFNNs) is investigated in this paper. For this purpose, we propose the use of a generalised version of the standard RBFNN, based on the ...

Comments

Information & Contributors

Information

Published In

Neural Networks Volume 13, Issue 10

Dec. 2000

116 pages

ISSN:0893-6080

Editor:
Stephen Grossberg
Boston Univ., Boston, MA

Issue’s Table of Contents

Publisher

Elsevier Science Ltd.

United Kingdom

Publication History

Published: 01 December 2000

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

12
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Luo SCheng J(2017)VPMCD based novelty detection method on and its application to fault identification for local characteristic-scale decompositionCluster Computing10.1007/s10586-017-0932-220:4(2955-2965)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10586-017-0932-2
Nayak PMishra SDash PBisoi R(2016)Comparison of modified teaching---learning-based optimization and extreme learning machine for classification of multiple power signal disturbancesNeural Computing and Applications10.1007/s00521-015-2010-027:7(2107-2122)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s00521-015-2010-0
Lu JHu HBai Y(2015)Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithmNeurocomputing10.1016/j.neucom.2014.10.065152:C(305-315)Online publication date: 25-Mar-2015
https://dl.acm.org/doi/10.1016/j.neucom.2014.10.065
Wang SYu JLapira ELee J(2013)A modified support vector data description based novelty detection approach for machinery componentsApplied Soft Computing10.1016/j.asoc.2012.11.00513:2(1193-1205)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1016/j.asoc.2012.11.005
Liu BXiao YCao LHao ZDeng F(2013)SVDD-based outlier detection on uncertain dataKnowledge and Information Systems10.1007/s10115-012-0484-y34:3(597-618)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1007/s10115-012-0484-y
Barreto GFrota R(2013)A unifying methodology for the evaluation of neural network models on novelty detection tasksPattern Analysis & Applications10.1007/s10044-011-0265-316:1(83-97)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1007/s10044-011-0265-3
Biswal BDash PMishra S(2009)Non-stationary power signal classification using local linear radial basis function neural networksInternational Journal of Knowledge-based and Intelligent Engineering Systems10.5555/1609984.160998913:2(79-90)Online publication date: 1-Apr-2009
https://dl.acm.org/doi/10.5555/1609984.1609989
Chandola VBanerjee AKumar V(2009)Anomaly detectionACM Computing Surveys10.1145/1541880.154188241:3(1-58)Online publication date: 30-Jul-2009
https://dl.acm.org/doi/10.1145/1541880.1541882
Ma LWahab ANg GErdogan S(2008)An experimental study of the extended NRBF regression model and its enhancement for classification problemNeurocomputing10.1016/j.neucom.2007.12.01172:1-3(458-470)Online publication date: 1-Dec-2008
https://dl.acm.org/doi/10.1016/j.neucom.2007.12.011
Singh SMarkou M(2004)An Approach to Novelty Detection Applied to the Classification of Image RegionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2004.126966516:4(396-407)Online publication date: 1-Apr-2004
https://dl.acm.org/doi/10.1109/TKDE.2004.1269665
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Index Terms

Recommendations

Classification by evolutionary generalised radial basis functions

Classification by Evolutionary Generalized Radial Basis Functions

Generalised Gaussian radial basis function neural networks

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations