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Condition: As New. Unread book in perfect condition.
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Computational Intelligence for Big Data Analysis. Frontier Advances and Applications.
Published by Switzerland, Springer International Publishing., 2015
ISBN 10: 3319165976 ISBN 13: 9783319165974
Language: English
Seller: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germany
Association Member: GIAQ ILAB VDA
Seller rating 4 out of 5 stars
US$ 18.51
Convert currencyUS$ 33.69 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketAufl. 2015. 235 mm x 155 mm. XIX, 265 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Stamped. Adaptation, Learning and Optimization 19.
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Add to basketHardcover. Condition: Used; Very Good. Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine.
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Grey Data Analysis: Methods, Models and Applications (Computational Risk Management)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seismic Data Analysis by Using Computational Intelligence: Neural Network and Genetic Algorithm Applications for Evaluating Earthquake Key Parameters
Published by LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838383877 ISBN 13: 9783838383873
Language: English
US$ 139.71
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Add to basketPaperback. Condition: Like New. Like New. book.
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Hidden Markov Models with Applications in Computational Biology : Model Extensions and Advanced Analysis of DNA Microarray Data
Published by Südwestdeutscher Verlag Für Hochschulschriften, 2013
ISBN 10: 3838136047 ISBN 13: 9783838136042
Language: English
US$ 92.43
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Standard first-order Hidden Markov Models (HMMs) are very popular tools for the analysis of sequential data in applied sciences. HMMs are versatile and structurally simple models enabling probabilistic modeling based on a sound theoretical grounding. In contrast to the broad usage of first-order HMMs, applications of higher-order HMMs are very rare, but they have been proven to be powerful extensions of first-order HMMs including applications in speech recognition, image segmentation or computational biology. This book provides the first easily accessible and comprehensive extension of the algorithmic basics of first-order HMMs to higher-order HMMs coupled with practical applications in computational biology. The book starts with a theoretical part developing the algorithmic basics of higher-order HMMs and two novel model extensions (i) parsimonious higher-order HMMs and (ii) HMMs with scaled transition matrices. The second part considers applications of these models to the analysis of different DNA microarray data sets followed by a detailed discussion. The book addresses readers having basic knowledge on first-order HMMs interested to gain more insights on higher-order HMMs.
