Machine Learning Bayesian Optimization (27 results)
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Hardcover. Condition: Brand New. 2nd edition. 1071 pages. 9.25x7.50x2.25 inches. In Stock.
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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..
- Hardcover
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Condition: New. 2020. 2nd Edition. Hardcover. . . . . .
- Hardcover
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Hardcover. Condition: Brand New. 2nd edition. 1071 pages. 9.25x7.50x2.25 inches. In Stock.
Language: English
Published by Elsevier Science Publishing Co Inc Mär 2020 2020
- Hardcover
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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 likelihoo…d 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.
- Hardcover
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Condition: New. 2020. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
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Buch. Condition: Neu. Machine Learning | A Bayesian and Optimization Perspective | Sergios Theodoridis | Buch | Einband - fest (Hardcover) | 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.
- Hardcover
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Hardcover. Condition: Brand New. 1st edition. 1062 pages. 9.50x7.75x2.00 inches. In Stock.
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US$ 133.48
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paperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2025-04 Pages: 352 Publisher: Tsinghua University Press In the field of machine learning. the core goal of optimization is to obtain the most accurate prediction results with the least computational resourceswhether it's planning the shortest delivery route. determ…ining pricing strategies. or generating the most accurate recommendations. When traditional methods prove inadequate due to inefficiency or excessive cost. Bayesian optimization can leverage the.
Language: English
- Softcover
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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.
Published by Machinery Industry Press 2022
- Softcover
Seller: liu xing, Nanjing, JS, Chinaliu xing
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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.
Machine learning: Bayesian and optimization methods (English version2nd edition of the original book)(Chinese Edition)
[ XI ] XI GE ER SI XI AO DUO LI DI SI ( Sergios Theodoridis )
- Softcover
Seller: liu xing, Nanjing, JS, Chinaliu xing
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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. includin…g mean squares. *small squares and maximum likelihood methods. ridge regression. Bayesian decision theory classification. logistic regression. and decision trees.?Then introduce the latest techn.
- Softcover
Seller: liu xing, Nanjing, JS, Chinaliu xing
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paperback. 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 .
- Hardcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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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 t…he 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.
