Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic, Suzuki Joe

Автор: Suzuki
Название: Kernel Methods for Machine Learning with Math and Python
ISBN: 9811904006 ISBN-13(EAN): 9789811904004
Издательство: Springer
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Цена: 41920.00 T
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Описание: The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: * The content is written in an easy-to-follow and self-contained style. * The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. * The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. * Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. * Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. * This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.

Автор: Shi Yu; L?on-Charles Tranchevent; Bart Moor; Yves
Название: Kernel-based Data Fusion for Machine Learning
ISBN: 3642267513 ISBN-13(EAN): 9783642267512
Издательство: Springer
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Цена: 130590.00 T
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Описание: Data fusion problems arise in many different fields. This book provides a specific introduction to solve data fusion problems using support vector machines. The reader will require a good knowledge of data mining, machine learning and linear algebra.

Автор: Kung
Название: Kernel Methods and Machine Learning
ISBN: 110702496X ISBN-13(EAN): 9781107024960
Издательство: Cambridge Academ
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Цена: 90810.00 T
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Описание: Containing numerous algorithms and major theorems, this step-by-step guide covers the fundamentals of kernel-based learning theory. Including over two hundred problems and real-world examples, it is an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Автор: Muandet, Krikamol Fukumizu, Kenji Sriperumbudur, Bharath Scholkopf, Bernhard
Название: Kernel mean embedding of distributions:
ISBN: 1680832883 ISBN-13(EAN): 9781680832884
Издательство: Неизвестно
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Цена: 91040.00 T
Наличие на складе: Невозможна поставка.
Описание: This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.

Автор: Jun-Bao Li; Shu-Chuan Chu; Jeng-Shyang Pan
Название: Kernel Learning Algorithms for Face Recognition
ISBN: 1493952129 ISBN-13(EAN): 9781493952120
Издательство: Springer
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Цена: 104480.00 T
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Описание: This book covers the framework of kernel based face recognition. It discusses the advanced kernel learning algorithms and its application on face recognition. The book also focuses on the theoretical deviation, the system framework and experiments.

Автор: Jose C. Principe
Название: Information Theoretic Learning
ISBN: 1441915699 ISBN-13(EAN): 9781441915696
Издательство: Springer
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Цена: 139750.00 T
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Описание: This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Автор: Jose C. Principe
Название: Information Theoretic Learning
ISBN: 1461425859 ISBN-13(EAN): 9781461425854
Издательство: Springer
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Цена: 144410.00 T
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Описание: This book is the first cohesive treatment of ITL algorithms to adapt linear or nonlinear learning machines both in supervised and unsupervised paradigms. It compares the performance of ITL algorithms with the second order counterparts in many applications.

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642045324 ISBN-13(EAN): 9783642045325
Издательство: Springer
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Цена: 139310.00 T
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.

Автор: Catarina Silva; Bernadete Ribeiro
Название: Inductive Inference for Large Scale Text Classification
ISBN: 3642261345 ISBN-13(EAN): 9783642261343
Издательство: Springer
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Цена: 113180.00 T
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Описание: This book explains and illustrates key methods in inductive inference in large scale text classification, especially kernel approaches. It covers a series of new techniques to enhance, scale and distribute text classification tasks.

Автор: Tetsuya Hoya
Название: Artificial Mind System
ISBN: 3642424724 ISBN-13(EAN): 9783642424724
Издательство: Springer
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Цена: 139750.00 T
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Описание: "Artificial Mind System" exposes the reader to a broad spectrum of interesting areas in general brain science and mind-oriented studies. With a view that "the mind is a system always evolving", ideas inspired by many branches of studies related to brain science are integrated within the text, i.e.

Автор: Ricardo Louren?o; Nuno Louren?o; Nuno Horta
Название: AIDA-CMK: Multi-Algorithm Optimization Kernel Applied to Analog IC Sizing
ISBN: 3319159542 ISBN-13(EAN): 9783319159546
Издательство: Springer
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Цена: 60940.00 T
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Описание: The proposed solution implements three approaches to multi-objective multi-constraint optimization, namely, an evolutionary approach with NSGAII, a swarm intelligence approach with MOPSO and stochastic hill climbing approach with MOSA.

Автор: Gramacki
Название: Nonparametric Kernel Density Estimation and Its Computational Aspects
ISBN: 3319716875 ISBN-13(EAN): 9783319716879
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book describes computational problems related to kernel density estimation (KDE)-one of the most important and widely used data smoothing techniques. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects.