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Type-2 Fuzzy Graphical Models for Pattern Recognition [electronic resource] / by Jia Zeng, Zhi-Qiang Liu.

By: Zeng, Jia [author.].
Contributor(s): Liu, Zhi-Qiang [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 591Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2015Description: XIII, 201 p. 112 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783662446904.Subject(s): Engineering | Artificial intelligence | Pattern recognition | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Pattern Recognition | Signal, Image and Speech ProcessingAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- 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.
In: Springer eBooksSummary: 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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Introduction -- 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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