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Machine Learning Bayesian Optimization by Theodoridis Sergios (21 results)
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Hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Machine Learning: A Bayesian and Optimization Perspective
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Add to basketCondition: Fine. Light scuffs & scratches to the hardcover. Faint marks to the edges of the textblock. Content is in very good, clean condition/like new.
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Machine Learning: A Bayesian and Optimization Perspective
Language: English
Published by Academic Press 2020-03-20, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
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Machine Learning : A Bayesian and Optimization Perspective
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Machine Learning: A Bayesian and Optimization Perspective
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Machine Learning : A Bayesian and Optimization Perspective
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Add to basketHardcover. Condition: Brand New. 2nd edition. 1071 pages. 9.25x7.50x2.25 inches. In Stock.
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Machine Learning: A Bayesian and Optimization Perspective
Language: English
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Condition: New. 2020. 2nd Edition. Hardcover. . . . . .
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Condition: New. Presents the physical reasoning, mathematical modeling and algorithmic implementation of each method Updates on the latest trends, including sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces,.
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Machine Learning: A Bayesian and Optimization Perspective
Language: English
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Condition: New. 2020. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Machine Learning : A Bayesian and Optimization Perspective
Language: English
Published by Elsevier Science Publishing Co Inc Mär 2020, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Buch. Condition: Neu. Neuware - Machine Learning: A Bayesian and Optimization Perspective, 2nd edition, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.
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Machine Learning | A Bayesian and Optimization Perspective
Language: English
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Buch. Condition: Neu. Machine Learning | A Bayesian and Optimization Perspective | Sergios Theodoridis | Buch | Einband - flex.(Paperback) | Englisch | 2020 | Elsevier Science Publishing Co Inc | EAN 9780128188033 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Add to basketHardcover. Condition: Brand New. 1st edition. 1062 pages. 9.50x7.75x2.00 inches. In Stock.
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Machine Learning: Bayesian and Optimization Methods (Original Book 2nd Edition)(Chinese Edition)
Language: English
ISBN 10: 7111692578 ISBN 13: 9787111692577
paperback. Condition: New. Paperback.Pub Date:2022-01-01 Pages:828 Language:Chinese Publisher:Machinery Industry Press Machine Learning: Bayesian and Optimization Methods (Original Book 2nd Edition) for all important machine learning methods and recent research The trend has been deeply explored. By explaining the two pillars of supervised learningregression and classification. these complicated methods are opened up one by one from a panoramic perspective. forming a clear machine learning knowledge system. The new edi.
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Machine Learning: Bayesian and Optimization Methods (Original Book 2nd Edition)(Chinese Edition)
Published by Machinery Industry Press, 2022
ISBN 10: 7111692578 ISBN 13: 9787111692577
paperback. Condition: New. Paperback.Pub Date:2022-01-01 Pages:828 Language:Chinese Publisher:Machinery Industry Press Machine Learning: Bayesian and Optimization Methods (Original Book 2nd Edition) for all important machine learning methods and recent research The trend has been deeply explored. By explaining the two pillars of supervised learningregression and classification. these complicated methods are opened up one by one from a panoramic perspective. forming a clear machine learning knowledge system. The new edi.
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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2020-12-01 Pages:1152 Publisher: Machinery Industry Press This book introduces the two pillars of supervised learning-regression and classification-and brings machine learning into a unified perspective to discuss.?The book first discusses basic knowledge. including mean squares. *small squares and maximum likelihood methods. ridge regression. Bayesian decision theory classification. logistic regression. and decision trees.?Then introduce the latest techn.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches -which are based on optimization techniques - together with the Bayesian inference approach, whose essence lies in the use of a hierarchy of probabilistic models.The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models.
