Elkan's k-means algorithm for graphs
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
People detection using color and depth images
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Learning and inference order in structured output elements classification
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Transfer learning approach to debt portfolio appraisal
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
An analysis of content-based classification of audio signals using a fuzzy c-means algorithm
Multimedia Tools and Applications
A real-world deployment of the COACH prompting system
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.This edition includes many more worked examples and diagrams (in two colour) to help give greater understanding of the methods and their application. Computer-based problems will be included with MATLAB code. The accompanying book contains extra worked examples and MATLAB code of all the examples used in this book. This specially priced set includes a copy of Theodoridis/Koutroumbas, Pattern Recognition 4e and Theodoridis/Pikrakis/Koutroumbas/Cavouras, Introduction to Pattern Recognition: A Matlab Approach.The main text providesbreadth and depth ofcoverage of pattern recognition theory and application,including modern topics likenon-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, and combining clustering algorithms. Together with worked examples, exercises, and Matlab applications it provides the most comprehensive coverage currently available. The accompanying manual includes MATLAB code of the most common methods and algorithms in the book, together with a descriptive summary and solved problems, and including real-life data sets in imaging and audio recognition.