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Markov Models for Pattern Recognition

. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the;

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Markov Random Field Modeling in Image Analysis

in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses;

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Internet-Scale Pattern Recognition

keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational;

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Internet-Scale Pattern Recognition

keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational;

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Hidden Semi-Markov Models

the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions;

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Bio-Inspired Hybrid Intelligent Systems For Image Analysis And Pattern Recognition

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired;

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Computational Intelligence for Pattern Recognition

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and;

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Pattern Recognition and Computational Intelligence Techniques Using Matlab

This book presents the complex topic of using computational intelligence for pattern recognition in a straightforward and applicable way;

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Inference for Hidden Markov Models and related Models

Inference for Hidden Markov Models and related Models is een boek van Cuvillier Verlag;

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Perspectives on Pattern Recognition

for real time applications and quantitative bioimage analysis using pattern recognition.;

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Image Processing

in computer vision. The updated version presents new sections on compression via JPEG and MPEG, fractals, fuzzy logic recognition, hidden Markov;

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Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms;

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Essentials of Pattern Recognition

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester;

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Pattern Recognition

This book constitutes the refereed proceedings of the 35th German Conference on Pattern Recognition, GCPR 2013, held in Saarbrücken;

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Pattern Recognition

This book constitutes the refereed proceedings of the 39th German Conference on Pattern Recognition, GCPR 2017, held in Basel, Switzerland;

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Similarity-Based Pattern Recognition

This book constitutes the proceedings of the First International Workshop on Similarity Based Pattern Recognition, SIMBAD 2011, held;

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Pattern Recognition In Computati

discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and;

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Pattern Recognition

pattern analysis for multidisciplinary computational anatomy; multi-sensor for action and gesture recognition; towards the automatic data;

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Markov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and;

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Markov Chains and Decision Processes for Engineers and Managers

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and;

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Dissimilarity Representation For Pattern Recognition, The

authors have crafted an essential book for every researcher and systems designer studying or developing pattern recognition systems.;

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Discriminative Learning for Speech Recognition

speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A;

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Rough-Fuzzy Pattern Recognition

pattern recognition models. Based on the existing as well as new results, the book is structured according to the major phases of a pattern;

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Markov Chains

highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain;

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Pattern Recognition

This book constitutes the refereed proceedings of the 36th German Conference on Pattern Recognition, GCPR 2014, held in Munster, Germany;

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Neural Networks For Pattern Recognition

recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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