Type-2 Fuzzy Graphical Models for Pattern Recognition electronic resource by Jia Zeng, Zhi-Qiang Liu.
Material type: TextSeries: Studies in Computational IntelligencePublication details: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Description: XIII, 201 p. 112 illus. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783662446904Subject(s): engineering | Artificial intelligence | Pattern Recognition | Computational Intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Pattern Recognition | Signal, Image and Speech ProcessingDDC classification: 006.3 LOC classification: Q342Online resources: Click here to access onlineIntroduction -- Probabilistic Graphical Models -- Type-2 Fuzzy Sets for Pattern Recognition -- Type-2 Fuzzy Gaussian Mixture Models -- Type-2 Fuzzy Hidden Moarkov Models -- Type-2 Fuzzy Markov Random Fields -- Type-2 Fuzzy Topic Models -- Conclusions and FutureWork.
This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.
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