Active learning for Probabilistic Neural Networks

Bülent Bolat; Tülay Yildirim

doi:10.1007/11539087_13

Active learning for Probabilistic Neural Networks

Bülent Bolat^*, Tülay Yildirim

^*Corresponding author for this work

Yildiz Technical University

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

In many neural network applications, the selection of best training set to represent the entire sample space is one of the most important problems. Active learning algorithms in the literature for neural networks are not appropriate for Probabilistic Neural Networks (PNN). In this paper, a new active learning method is proposed for PNN. The method was applied to several benchmark problems.

Original language	English
Pages (from-to)	110-118
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3610
Issue number	PART I
DOIs	https://doi.org/10.1007/11539087_13
Publication status	Published - 2005
Externally published	Yes
Event	First International Conference on Natural Computation, ICNC 2005 - Changsha, China Duration: 27 Aug 2005 → 29 Aug 2005

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10.1007/11539087_13

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title = "Active learning for Probabilistic Neural Networks",

abstract = "In many neural network applications, the selection of best training set to represent the entire sample space is one of the most important problems. Active learning algorithms in the literature for neural networks are not appropriate for Probabilistic Neural Networks (PNN). In this paper, a new active learning method is proposed for PNN. The method was applied to several benchmark problems.",

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AU - Yildirim, Tülay

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AB - In many neural network applications, the selection of best training set to represent the entire sample space is one of the most important problems. Active learning algorithms in the literature for neural networks are not appropriate for Probabilistic Neural Networks (PNN). In this paper, a new active learning method is proposed for PNN. The method was applied to several benchmark problems.

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DO - 10.1007/11539087_13

M3 - Conference article

AN - SCOPUS:26844535021

SN - 0302-9743

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EP - 118

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - First International Conference on Natural Computation, ICNC 2005

Y2 - 27 August 2005 through 29 August 2005

ER -

Active learning for Probabilistic Neural Networks

Abstract

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