Machine Learning Bayesian Optimization (24 results)
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- Hardcover
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- Hardcover
Seller: Hay-on-Wye Booksellers, Hay-on-Wye, HEREF, United KingdomHay-on-Wye Booksellers
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- Hardcover
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- Hardcover
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- Softcover
Seller: liu xing, Nanjing, JS, Chinaliu xing
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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.
- Hardcover
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- Hardcover
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Condition: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
- Hardcover
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrelandKennys Bookshop and Art Galleries Ltd.
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Condition: New. 2020. 2nd Edition. Hardcover. . . . . .
- Hardcover
Seller: moluna, Greven, Germanymoluna
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Gebunden. 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..
Language: English
Published by Elsevier Science Publishing Co Inc Mär 2020, 2020
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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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. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization. Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, 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. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python. The chapters are 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 courses on sparse modeling, deep learning, and probabilistic graphical models. New to this edition: - Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs). - Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.
- Hardcover
Seller: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Condition: New. 2020. 2nd Edition. Hardcover. . . . . . Books ship from the US and Ireland.
- Hardcover
Seller: preigu, Osnabrück, Germanypreigu
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Buch. Condition: Neu. Machine Learning | A Bayesian and Optimization Perspective | Sergios Theodoridis | Buch | Einband - fest (Hardcover) | Englisch | 2020 | Elsevier Science | EAN 9780128188033 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter:… preigu.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 2nd edition. 1071 pages. 9.25x7.50x2.25 inches. In Stock.
- Hardcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Hardcover. Condition: Brand New. 1st edition. 1062 pages. 9.50x7.75x2.00 inches. In Stock.
Language: English
- 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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US$ 316.08
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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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US$ 281.58
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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 .
Seller: Hakone Books, Fujisawa, KANAG, JapanHakone Books
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This O'Reilly Japan technical guide introduces machine learning principles specifically tailored for web optimization strategies. It covers essential techniques such as A/B testing, various metaheuristics, and bandit algorithms, progressing to more advanced topics like Bayesian optimization. Ideal for web developers, data scient…ists, and marketers looking to leverage ML to improve website performance and user experience, this volume provides practical insights into applying these powerful algorithms. Note: The text is entirely in Japanese. Brand New Japanese Edition. Ships worldwide from Japan via Japan Post. Expedited shipping via FedEx available.
