Pattern recognition : from classical to modern approaches
著者
書誌事項
Pattern recognition : from classical to modern approaches
World Scientific, c2001
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and applications, as well as up-to-date information and an extensive bibliography, Pattern Recognition: From Classical to Modern Approaches is a very useful resource.
目次
- Pattern recognition - evolution of methodologies and data mining, A. Pal and S.K. Pal
- adaptive stochastic algorithms for pattern classification, M.A.L. Thathachar and P.S. Sastry
- shape in images, K.V. Mardia
- decision trees for classification - a review and some new results, R. Kothari and M. Dong
- syntactic pattern recognition, A.K. Majumder and A.K. Ray
- fuzzy sets as a logic canvas for pattern recognition, W. Pedrycz and N. Pizzi
- neural network based pattern recognition, V. David Sanchez
- networks of spiking neurons in data mining, K. Cios and D.M. Sala
- genetic algorithms, pattern classification and neural networks design, S. Bandyopadhyay et al
- rough sets in pattern recognition, A. Skowron and R. Swiniarski
- automated generation of qualitative representations of complex objects by hybrid soft-computing methods, E.H. Ruspini and I.S. Zwir)
- writing speed and writing sequence invariant on-line handwriting recognition, S-H Cha and S.N. Srihari
- tongue diagnosis based on biometric pattern recognition technology, K. Wang et al
- and other papers.
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