Learning Hidden Markov Models Using Probabilistic Matrix Factorization
Hidden Markov Models (HMM) provide an excellent tool for building probabilistic graphical models to describe a sequence of observable entities. The parameters of a HMM are estimated using the Baum–Welch algorithm, which scales linearly with the sequence l
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Katsutoshi Yada Editor
Data Mining for Service
Studies in Big Data Volume 3
Series editor Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland e-mail: [email protected]
For further volumes: http://www.springer.com/series/11970
About this Series The series ‘‘Studies in Big Data’’ (SBD) publishes new developments and advances in the various areas of Big Data- quickly and with a high quality. The intent is to cover the theory, research, development, and applications of Big Data, as embedded in the fields of engineering, computer science, physics, economics and life sciences. The books of the series refer to the analysis and understanding of large, complex, and/or distributed data sets generated from recent digital sources coming from sensors or other physical instruments as well as simulations, crowd sourcing, social networks or other internet transactions, such as emails or video click streams and other. The series contains monographs, lecture notes and edited volumes in Big Data spanning the areas of computational intelligence incl. neural networks, evolutionary computation, soft computing, fuzzy systems, as well as artificial intelligence, data mining, modern statistics and Operations research, as well as self-organizing systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.
Katsutoshi Yada Editor
Data Mining for Service
123
Editor Katsutoshi Yada Faculty of Commerce Kansai University Osaka Japan
ISSN 2197-6503 ISBN 978-3-642-45251-2 DOI 10.1007/978-3-642-45252-9
ISSN 2197-6511 (electronic) ISBN 978-3-642-45252-9 (eBook)
Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013957989 Springer-Verlag Berlin Heidelberg 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this public
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