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Investigating feedforward neural networks with respect to the rejection of spurious patterns

Authors:

G. C. Vasconcelos,

M. C. Fairhurst,

D. L. BissetAuthors Info & Claims

Pattern Recognition Letters, Volume 16, Issue 2

Pages 207 - 212

https://doi.org/10.1016/0167-8655(94)00092-H

Published: 01 February 1995 Publication History

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Hendrickx KPerini LVan der Plas DMeert WDavis J(2024)Machine learning with a reject option: a surveyMachine Language10.1007/s10994-024-06534-x113:5(3073-3110)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06534-x
Chen XTan DGupta PBromuri S(2023)Pair-wise selective classification with dynamic sampling for shipment importer predictionProceedings of the 2023 15th International Conference on Machine Learning and Computing10.1145/3587716.3587741(152-157)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3587716.3587741
Kerner HWellington DWagstaff KBell JKwan CAmor H(2019)Novelty detection for multispectral images with application to planetary explorationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33019484(9484-9491)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33019484
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Index Terms

Investigating feedforward neural networks with respect to the rejection of spurious patterns
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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Published In

Pattern Recognition Letters Volume 16, Issue 2

Feb. 1995

106 pages

ISSN:0167-8655

Editors:
E. Backer
Delft Univ. of Technology, Delft, Netherlands
,
E. S. Gelsema
Erasmus Univ., Rotterdam, Netherlands

Issue’s Table of Contents

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 February 1995

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Hendrickx KPerini LVan der Plas DMeert WDavis J(2024)Machine learning with a reject option: a surveyMachine Language10.1007/s10994-024-06534-x113:5(3073-3110)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1007/s10994-024-06534-x
Chen XTan DGupta PBromuri S(2023)Pair-wise selective classification with dynamic sampling for shipment importer predictionProceedings of the 2023 15th International Conference on Machine Learning and Computing10.1145/3587716.3587741(152-157)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3587716.3587741
Kerner HWellington DWagstaff KBell JKwan CAmor H(2019)Novelty detection for multispectral images with application to planetary explorationProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33019484(9484-9491)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33019484
Gamelas Sousa RRocha Neto ACardoso JBarreto G(2015)Robust classification with reject option using the self-organizing mapNeural Computing and Applications10.1007/s00521-015-1822-226:7(1603-1619)Online publication date: 1-Oct-2015
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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
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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
Zhu HTang XLiu P(2006)An MLP-orthogonal Gaussian mixture model hybrid model for Chinese bank check printed numeral recognitionInternational Journal on Document Analysis and Recognition10.5555/2722895.27230618:1(27-34)Online publication date: 1-Apr-2006
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Markou MSingh S(2003)Novelty detectionSignal Processing10.1016/j.sigpro.2003.07.01983:12(2499-2521)Online publication date: 1-Dec-2003
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Improving the Performance of Feedforward Neural Networks by Noise Injection into Hidden Neurons

Application of SFG in Learning Algorithms of Neural Networks

Approximation of algebraic and trigonometric polynomials by feedforward neural networks

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