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Kernel Mode Decomposition and the Programming of Kernels

alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem;

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Generalized Mercer Kernels and Reproducing Kernel Banach Spaces

-posed and of easy implementation. First the authors verify many advanced properties of the general RKBSs such as density, continuity;

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

and is considered one of the most flexible methods for error-tolerant graph matching.This book focuses on graph kernel functions that are;

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Asymmetric Kernel Smoothing

kernel functions. Further, it discusses the statistical properties of estimators and test statistics using asymmetric kernels. The topics;

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Beginning Linux Programming 4th

programming, using MySQL, writing applications for the GNOME or the KDE desktop, writing device drivers, POSIX Threads, and kernel programming for the;

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Linux Kernel Programming Part 2 - Char Device Drivers and Kernel Synchronization

- Char Device Drivers and Kernel Synchronization is an ideal companion guide to the Linux Kernel Programming book. This book provides a;

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Theory of Reproducing Kernels and Applications

kernels with applications to numerical and practical solutions of bounded linear operator equations.In Chapter 4, the numerical real inversion;

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Kernel-based Approximation Methods Using Matlab

In an attempt to introduce application scientists and graduate students to the exciting topic of positive definite kernels and radial basis;

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Advances in Dual Integral Equations

Legendre functions and mixed kernels involving Bessel functions. Researchers in the theory of elasticity, fluid dynamics, and mathematical physics;

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Data-Variant Kernel Analysis

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine;

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Kernels For Structured Data

This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be;

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Cambridge Tracts in Mathematics

of applicability of the theory. Special attention is paid to the singular functions of non-symmetric kernels and to obtaining as strong results as possible;

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Kernel Methods and Estimation of Extreme Value Index

propose two new asymmetric kernels called Birnbaum-Saunders and Lognormal kernels. These two kernels can be applied to the estimation of the;

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Sobolev Inequalities, Heat Kernels under Ricci Flow, and the Poincare Conjecture

kernel estimates to study Ricci flows, especially with surgeries. The author explains key ideas, difficult proofs, and important applications;

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Kernel Methods for Pattern Analysis

students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a;

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Facial Expression Recognition

of human facial expressions and kernel principal component analysis (kernel PCA) with fractional polynomial power models. A mean vector of features;

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Concept Techs Models Computer Program

related kernel languages, this approach allows programmer and student to grasp the underlying unity of programming. The book has many program;

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Linux Kernel Programming

Brand-new edition of the comprehensive Linux kernel reference - 40% of material revised. Now updated throughout to cover Linux 2.4.;

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Heat Kernels and Dirac Operators

its generalizations (due to the authors and J.-M. Bismut) were presented, using an explicit geometric construction of the heat kernel of a;

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Kernel Methods for Remote Sensing Data Analysis

Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have;

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Stochastic Linear Programming Algorithms

. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for;

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Creativity in Load-Balance Schemes for Multi/Many-Core Heterogeneous Graph Computing

''This book explores the need for HPC computing in network science & network science, core kernels for complex network analysis;

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An Introduction to the Theory of Reproducing Kernel Hilbert Spaces

Reproducing kernel Hilbert spaces have developed into an important tool in many areas, especially statistics and machine learning, and they;

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Arm-Based Microcontroller Multitasking Projects: Using the Freertos Multitasking Kernel

Most microcontroller-based applications nowadays are large, complex, and may require several tasks to share the MCU in multitasking;

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Linux System Programming

Write software that draws directly on services offered by the Linux kernel and core system libraries. With this comprehensive book, Linux;

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