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bayesian machine learning

Machine Learning A Bayesian and Optimization Perspective【電子書籍】 Sergios Theodoridis

Bayesian Reasoning and Machine Learning【電子書籍】 David Barber

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications【電子書籍】

洋書 Paperback, Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

Bayesian Tensor Decomposition for Signal Processing and Machine Learning Modeling, Tuning-Free Algorithms, and Applications【電子書籍】 Lei Cheng

Machine Learning A Bayesian and Optimization Perspective【電子書籍】[ Sergios Theodoridis ]

ショップ楽天Kobo電子書籍ストア

価格￥9,882

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商品の説明

＜p＞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.＜/p＞＜p＞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, statistic...

Bayesian Reasoning and Machine Learning【電子書籍】[ David Barber ]

ショップ楽天Kobo電子書籍ストア

価格￥9,506

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商品の説明

＜p＞Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instr...

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications【電子書籍】

ショップ楽天Kobo電子書籍ストア

価格￥8,945

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＜p＞This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. ＜strong＞Bayesian Reasoning and Gaussian Processes for Machine Learning Applications＜/strong＞ uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models.＜/p＞＜p＞FEATURES＜/p＞＜ul＞＜li＞＜/li＞＜li＞Contains recent advancements in machine learning＜/li＞＜li＞＜/li＞＜li＞Highlights applications of machin...

洋書 Paperback, Learning Bayesian Models with R: Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

ショップ Glomarket

価格￥8,591

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*** We ship internationally, so do not use a package forwarding service. We cannot ship to a package forwarding company address because of the Japanese customs regulation. If it is shipped and customs office does not let the package go, we do not make a refund. 【注意事項】 *** 特に注意してください。 *** ・個人ではない法人・団体名義での購入はできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。・お名前にカタカナが入っている場合法人である可能性が高いため当店システムから自動保留します。カタカナで記載が必要な場合はカタカナ変わりローマ字で記載してください。・お名前またはご住所が法人・団体名義（XX株式会社等）、商店名などを含めている場合、または電話番号が個人のものではない場合、税関から法人名義でみなされますのでご注意ください。・転送サービス会社への発送もできません。この場合税関で滅却されてもお客様負担になりますので御了承願います。 *** ・注文後品切れや価格変動でキャンセルされる場合がございますので予めご了承願います。・当店でご購入された商品は、原則として、「個...

Bayesian Tensor Decomposition for Signal Processing and Machine Learning Modeling, Tuning-Free Algorithms, and Applications【電子書籍】[ Lei Cheng ]

ショップ楽天Kobo電子書籍ストア

価格￥15,800

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商品の説明

＜p＞This book presents recent advances of Bayesian inference in structured tensor decompositions. It explains how Bayesian modeling and inference lead to tuning-free tensor decomposition algorithms, which achieve state-of-the-art performances in many applications, including＜/p＞＜ul＞＜li＞blind source separation;＜/li＞＜li＞social network mining;＜/li＞＜li＞image and video processing;＜/li＞＜li＞array signal processing; and,＜/li＞＜li＞wireless communications.＜/li＞＜/ul＞＜p＞The book begins with an introduction to the general topics of tensors and Bayesian theories. It then discusses probabilistic models of various structured tensor decompositions and their inference algorithms, with applications tailored for each tensor decomposition presented in the corresponding chapters. The book concludes by looking to the future, and areas where this research can be further developed.＜/p＞＜p＞Bayesian Tensor Decomposition for Signal Processing and Machine Learning is suita...

Machine Learning A Bayesian and Optimization Perspective【電子書籍】[ Sergios Theodoridis ]

ショップ楽天Kobo電子書籍ストア

価格￥10,382

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商品の説明

＜p＞＜strong＞Machine Learning: A Bayesian and Optimization Perspective, 2nd edition＜/strong＞, 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.＜/p＞＜p＞This palette of techniques concludes with an extended chapter on neural netwo...

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