Hybrid Methods In Pattern RecognitionHorst Bunke, Abraham Kandel World Scientific, 2002. gada 22. maijs - 336 lappuses The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago. A large number of different approaches have been proposed. Hybrid methods aim at combining the advantages of different paradigms within a single system.Hybrid Methods in Pattern Recognition is a collection of articles describing recent progress in this emerging field. It covers topics such as 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, the combination of symbolic and subsymbolic learning, and others. Also included is recent work on multiple classifier systems. Furthermore, the book deals with applications in on-line and off-line handwriting recognition, remotely sensed image interpretation, fingerprint identification, and automatic text categorization. |
Saturs
| 1 | |
Neural Networks for Structural Pattern Recognition | 33 |
Clustering for Hybrid Systems | 75 |
Combining Neural Networks and Hidden Markov Models | 113 |
Multiple Classifier Systems | 171 |
Applications of Hybrid Systems | 253 |
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accuracy Analysis and Machine average backpropagation bigrams bootstrapping Choquet integral Class classifier ensembles clustering algorithm combination function compatibility measure complex computed corresponding data set database decision decoding defined design methods discrete document error diversity estimation evaluation extracted feature extraction feature value feature vectors fingerprint image fuzzy input vector fuzzy measure fuzzy numbers Fuzzy Sets Gaussian half & half handwriting recognition Hidden Markov Models hybrid systems hyperboxes IEEE IEEE Transactions information granules interval Ishibuchi linguistic rules linguistic values Machine Intelligence matching score membership function neural net neuro-fuzzy neurons node number of clusters optimal parameters Pattern Analysis pattern recognition performance phase posterior probabilities problem Proc proposed recognition system representation ridge line following sample Section self-organizing maps Speech Recognition statistical strategy structure subset substring tests techniques tied-mixture tion trained neural network training patterns training set vector quantizer weights word graph word sequence tests