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Treewidth, Kernels, and Algorithms

This Festschrift was published in honor of Hans L. Bodlaender on the occasion of his 60th birthday. The 14 full and 5 short contributions;

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Parallel Algorithms in Computational Science and Engineering

and computationally demanding applications. The first few chapters explore algorithms, numerical techniques, and their parallel formulations;

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Digital Image Warping

, mesh warping, and special effects. The text, containing all original material, begins with the history of the field and continues with a review;

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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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Graph Kernels

perform predictions in both classification and regression settings.This monograph provides a review of existing graph kernels, their applications;

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Theory of Reproducing Kernels and Applications

applications.In Chapter 1, many concrete reproducing kernels are first introduced with detailed information. Chapter 2 presents a general and global;

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Heat Kernels And Spectral Theory

An advanced monograph on a central topic in the theory of differential equations, Heat Kernels and Spectral Theory investigates the theory;

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Tractability

Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to;

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Kernel Methods for Pattern Analysis

Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data;

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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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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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Stream Data Mining: Algorithms and Their Probabilistic Properties

heuristics, it highlights methods and algorithms that are mathematically justified. First, it describes how to adapt static decision trees to;

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Kernel Methods and Estimation of Extreme Value Index

This book is a blend of rigorous probability theories and interesting statistical methodologies. It contains two parts. In Part I we;

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Cambridge Tracts in Mathematics

, Hilbert and Schmidt. The treatment has been modernised by the systematic use of the Lebesgue integral, which considerably widens the range;

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Reasoning with Probabilistic and Deterministic Graphical Models

schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion;

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Reasoning with Probabilistic and Deterministic Graphical Models

schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion;

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Reasoning with Probabilistic and Deterministic Graphical Models

schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion;

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Machine Learning

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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Bridging The Gap Between Graph Edit Distance And Kernel Machines

In graph-based structural pattern recognition, the idea is to transform patterns into graphs and perform the analysis and recognition;

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The Convolution Transform

. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level;

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Half-discrete Hilbert-type Inequalities

homogeneous kernels of degree-one were considered. Since then, the theory of Hilbert-type discrete and integral inequalities is almost built by Prof;

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Weighted Norm Inequalities for Integral Transforms with Product Kernals

The book may be considered as a systematic and detailed analysis of a wide class of integral transforms with product kernels from the two;

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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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Machine Learning: A Practical Approach on the Statistical Learning Theory

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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Kernel Mode Decomposition and the Programming of Kernels

/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and;

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An Introduction to Laplacian Spectral Distances and Kernels

In geometry processing and shape analysis, several applications have been addressed through the properties of the Laplacian spectral;

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