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Hybrid methods in pattern recognition

A collection of articles describing progress in the field of hybrid methods in pattern recognition. It explores the combination of neural nets with fuzzy systems or hidden Markov models, neural networks for the processing of symbolic data structures, hybrid methods in data mining, and more.
Print Book, English, 2002
World Scientific, River Edge, NJ [u.a.], 2002
Aufsatzsammlung
XII, 324 S. graph. Darst.
9789810248321, 9810248326
248187300
Neuro-fuzzy systems: fuzzification of neural networks for classification problems, H. Ishibuchi and M. Nii; neural networks for structural pattern recognition - adaptive graphic pattern recognition -foundations and perspectives, G. Adorni et al; adaptive self-organizing map in the graph domain, S. Gunter and H. Bunke. Clustering for hybrid systems: from numbers to information granules - a study in unsupervised learning and feature analysis, A. Bargiela and W. Pedrycz. Combining neural networks and hidden Markov models: combination of hidden Markov models and neural networks for hybrid statistical pattern recognition, G. Rigoll; from character to sentences - a hybrid neuro-Markovian system for on-line handwriting recognition, T. Artieres et al. Multiple classifier systems: multiple classifier combination - lessons and next steps, T.K. Ho; design of multiple classifier systems, F. Roli and G. Giacinto; fusing neural networks through fuzzy integration, A. Verikas et al. Applications of hybrid systems: hybrid data mining methods in image processing, A. Klose and R. Kruse; robust fingerprint identification based on hybrid pattern recognition methods, D.-W. Jung and R.-H. Park; text categorization using learned document features, M. Junker et al.