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Author Rongfu Mao Haichao Zhu ♦ Linke Zhang ♦ Aizhi Chen
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Support vector machines ♦ Fault diagnosis ♦ Learning systems ♦ Algorithm design and analysis ♦ Intelligent networks ♦ Neural networks ♦ Artificial neural networks ♦ Machine learning ♦ Data engineering ♦ Artificial intelligence
Abstract Artificial neural networks are relevant to solve large sample problems and the learning performance may not be good in small sample conditions. Inspired by applications of posterior probability, a new neural network learning method based on posterior probability (PPNN) is proposed to improve small data set learning accuracy in this paper. Together with the techniques of creating new learning samples to fill up the gaps between original samples and using support vector machine (SVM) to obtain posterior probabilities, a novel neural network model whose inputs include the samples and their posterior probabilities is constructed. Simulation experiment and two real data application results indicate that learning accuracy can be significantly improved by the proposed algorithm involving very small data set. It provides a new feasible way to assist small data set neural network learning
Description Author affiliation: Inst. of Noise & Vibration, Naval Univ. of Eng., Wuhan (Rongfu Mao Haichao Zhu)
ISBN 0769525288
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-10-16
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 245.97 kB
Page Count 6
Starting Page 17
Ending Page 22

Source: IEEE Xplore Digital Library