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Machine Learning Bayesian Optimization (20 results)
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Condition: good. Gently used with minimal wear on the corners and cover. A few pages may contain light highlighting or writing, but the text remains fully legible. Dust jacket may be missing, and supplemental materials like CDs or codes may not be included. May be ex-library with library markings. Ships promptly!
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Machine Learning: A Bayesian and Optimization Perspective
Seller: Once Upon A Time Books, Siloam Springs, AR, U.S.A.
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hardcover. Condition: Good. This is a used book in good condition and may show some signs of use or wear . This is a used book in good condition and may show some signs of use or wear .
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Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
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Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks.
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Machine Learning: A Bayesian and Optimization Perspective
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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Machine Learning : A Bayesian and Optimization Perspective
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning: A Bayesian and Optimization Perspective
Seller: Hay-on-Wye Booksellers, Hay-on-Wye, HEREF, United Kingdom
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US$ 115.33
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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
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Machine Learning: A Bayesian and Optimization Perspective
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Language: English
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Machine Learning: A Bayesian and Optimization Perspective
Published by Elsevier Science Publishing Co Inc, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Language: English
Condition: New. 2020. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.
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Machine Learning : A Bayesian and Optimization Perspective
Published by Elsevier Science Jul 2020, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Language: English
US$ 125.68
Convert currencyUS$ 44.97 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketBuch. Condition: Neu. Neuware - 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, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling Provides case studies on a variety of topics, including protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, and more.
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Machine Learning: A Bayesian and Optimization Perspective
Published by Elsevier LTD, Oxford, 2020
ISBN 10: 0128188030 ISBN 13: 9780128188033
Language: English
US$ 119.45
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Add to basketGebunden. Condition: New. Über den AutorSergios Theodoridis is professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China.H.
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Machine Learning: Bayesian and Optimization Methods (Original Book 2nd Edition)(Chinese Edition)
ISBN 10: 7111692578 ISBN 13: 9787111692577
Language: English
US$ 297.75
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Add to basketpaperback. 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
US$ 297.75
Convert currencyUS$ 15.00 shipping from China to U.S.A.Quantity: 3 available
Add to basketpaperback. 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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US$ 316.08
Convert currencyUS$ 15.00 shipping from China to U.S.A.Quantity: 3 available
Add to basketpaperback. 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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Machine learning: Bayesian and Optimization Methods (English version)(Chinese Edition)
ISBN 10: 7111565266 ISBN 13: 9787111565260
US$ 281.58
Convert currencyUS$ 15.00 shipping from China to U.S.A.Quantity: 3 available
Add to basketpaperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2017-05-01 Pages: $number Publisher: Machinery Industry Press This book explores all major machine learning methods and new research trends. including probabilistic and deterministic methods and Bayesian inference methods. Among them. the classical methods include average small square filter. Kalman filter. stochastic approximation and online learning. Bayesian classification. decision .
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US$ 127.65
Convert currencyUS$ 44.67 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. 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.All major classical techniques: Mean/Least-Squares regression and filtering, Kalman filtering, stochastic approximation and online learning, Bayesian classification, decision trees, logistic regression and boosting methods.The latest trends: Sparsity, convex analysis and optimization, online distributed algorithms, learning in RKH spaces, Bayesian inference, graphical and hidden Markov models, particle filtering, deep learning, dictionary learning and latent variables modeling.Case studies - protein folding prediction, optical character recognition, text authorship identification, fMRI data analysis, change point detection, hyperspectral image unmixing, target localization, channel equalization and echo cancellation, show how the theory can be applied.MATLAB code for all the main algorithms are available on an accompanying website, enabling the reader to experiment with the code.
