Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification

Shen Yuong Wong, Keem Siah Yap

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Extreme Learning Machine (ELM) has drawn overwhelming attention from various fields notably in neural network researches for being an efficient algorithm. Using random computational hidden neurons, ELM shows faster learning speed over the traditional learning algorithms. Furthermore, it is stated that many types of hidden neurons which may not be neuron alike can be used in ELM as long as they are piecewise nonlinear. In this paper, we proposed a Constrained-Optimization-based ELM network structure implementing Bayesian framework in its hidden layer for learning and inference in a general form (denoted as C-BPP-ELM). Several benchmark data sets have been used to empirically evaluate the performance of the proposed model in pattern classification. The achieved results demonstrate that C-BPP-ELM outperforms the conventional ELM and the Constrained-Optimization-based ELM, and this in turn has validated the capability of ELM for being able to operate in a wide range of activation functions.

Original languageEnglish
Title of host publicationNeural Information Processing - 21st International Conference, ICONIP 2014, Proceedings
PublisherSpringer Verlag
Pages466-473
Number of pages8
Volume8836
ISBN (Electronic)9783319126425
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8836
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint

Extreme Learning Machine
Pattern Classification
Posterior Probability
Pattern recognition
Learning systems
Neurons
Neuron
Constrained optimization
Constrained Optimization
Alike
Activation Function
Network Structure
Learning algorithms
Learning Algorithm
Efficient Algorithms
Chemical activation
Neural Networks
Benchmark
Neural networks

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wong, S. Y., & Yap, K. S. (2014). Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification. In Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings (Vol. 8836, pp. 466-473). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836). Springer Verlag.
Wong, Shen Yuong ; Yap, Keem Siah. / Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification. Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Vol. 8836 Springer Verlag, 2014. pp. 466-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "Extreme Learning Machine (ELM) has drawn overwhelming attention from various fields notably in neural network researches for being an efficient algorithm. Using random computational hidden neurons, ELM shows faster learning speed over the traditional learning algorithms. Furthermore, it is stated that many types of hidden neurons which may not be neuron alike can be used in ELM as long as they are piecewise nonlinear. In this paper, we proposed a Constrained-Optimization-based ELM network structure implementing Bayesian framework in its hidden layer for learning and inference in a general form (denoted as C-BPP-ELM). Several benchmark data sets have been used to empirically evaluate the performance of the proposed model in pattern classification. The achieved results demonstrate that C-BPP-ELM outperforms the conventional ELM and the Constrained-Optimization-based ELM, and this in turn has validated the capability of ELM for being able to operate in a wide range of activation functions.",
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Wong, SY & Yap, KS 2014, Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification. in Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. vol. 8836, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8836, Springer Verlag, pp. 466-473.

Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification. / Wong, Shen Yuong; Yap, Keem Siah.

Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Vol. 8836 Springer Verlag, 2014. p. 466-473 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8836).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wong SY, Yap KS. Constrained–Optimization-based Bayesian posterior probability extreme learning machine for pattern classification. In Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings. Vol. 8836. Springer Verlag. 2014. p. 466-473. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).