Nonparametric Kernel Density Estimation and Its Computational Aspects, Artur Gramacki

Автор: Larry Wasserman
Название: All of Nonparametric Statistics
ISBN: 1441920447 ISBN-13(EAN): 9781441920447
Издательство: Springer
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Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets.

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: 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: 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: 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
Наличие на складе: Есть у поставщика Поставка под заказ.
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: R. Paul Drake
Название: High-Energy-Density Physics
ISBN: 3319677101 ISBN-13(EAN): 9783319677101
Издательство: Springer
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Цена: 102480.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The raw numbers of high-energy-density physics are amazing: shock waves at hundreds of km/s (approaching a million km per hour), temperatures of millions of degrees, and pressures that exceed 100 million atmospheres. This title surveys the production of high-energy-density conditions, the fundamental plasma and hydrodynamic models that can describe them and the problem of scaling from the laboratory to the cosmos. Connections to astrophysics are discussed throughout. The book is intended to support coursework in high-energy-density physics, to meet the needs of new researchers in this field, and also to serve as a useful reference on the fundamentals. Specifically the book has been designed to enable academics in physics, astrophysics, applied physics and engineering departments to provide in a single-course, an introduction to fluid mechanics and radiative transfer, with dramatic applications in the field of high-energy-density systems. This second edition includes pedagogic improvements to the presentation throughout and additional material on equations of state, heat waves, and ionization fronts, as well as problem sets accompanied by solutions.

Автор: Marshall
Название: Scalability, Density, and Decision Making in Cognitive Wireless Networks
ISBN: 1107015499 ISBN-13(EAN): 9781107015494
Издательство: Cambridge Academ
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Цена: 124610.00 T
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Описание: This cohesive treatment of cognitive radio and networking technology integrates information and decision theory to provide insight into relationships throughout all layers of networks and across all wireless applications. It features specific examples of decision-making structures and criteria required to extend network density and scaling to unprecedented levels.

Автор: Pereverzyev
Название: An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces
ISBN: 3030983153 ISBN-13(EAN): 9783030983154
Издательство: Springer
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Цена: 41920.00 T
Наличие на складе: Нет в наличии.
Описание: This textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks.

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: 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: 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: Suzuki Joe
Название: Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic
ISBN: 9811903972 ISBN-13(EAN): 9789811903977
Издательство: Springer
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Цена: 41920.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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 R 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.