Kernel Methods in Computer Vision
Kernel Methods in Computer Vision

Author: Christoph H. Lampert

Publisher: Now Publishers Inc

Published: 2009

Total Pages: 113

ISBN-13: 1601982682

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Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel-based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.

Kernel Methods and Machine Learning
Kernel Methods and Machine Learning

Author: S. Y. Kung

Publisher: Cambridge University Press

Published: 2014-04-17

Total Pages: 617

ISBN-13: 1139867636

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Offering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.

Kernel Methods in Bioengineering, Signal and Image Processing
Kernel Methods in Bioengineering, Signal and Image Processing

Author: Gustavo Camps-Valls

Publisher: IGI Global

Published: 2007-01-01

Total Pages: 431

ISBN-13: 1599040425

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"This book presents an extensive introduction to the field of kernel methods and real world applications. The book is organized in four parts: the first is an introductory chapter providing a framework of kernel methods; the others address Bioegineering, Signal Processing and Communications and Image Processing"--Provided by publisher.

Kernel Methods for Statistical Learning in Computer Vision and Pattern Recognition Applications
Kernel Methods for Statistical Learning in Computer Vision and Pattern Recognition Applications

Author: Refaat Mokhtar Mohamed

Publisher:

Published: 2005

Total Pages: 234

ISBN-13:

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The curse of dimensionality is a major difficulty which exists in the density function estimation with high dimensional data spaces. An active area of research in the pattern analysis community is to develop algorithms which cope with the dimensionality problem. The purpose of this dissertation is to present a kernel-based method for solving the density estimation problem as one of the fundamental problems in machine learning. The proposed method does not pay much attention to the dimensionality problem. The contribution of this dissertation has three folds: creating a reliable and efficient learning-based density estimation algorithm which is minimally dependent on the input space dimensionality, investigating efficient learning algorithms for the proposed approach, and investigating the performance of the proposed algorithm in different computer vision and pattern recognition applications.

Kernel Learning Algorithms for Face Recognition
Kernel Learning Algorithms for Face Recognition

Author: Jun-Bao Li

Publisher: Springer Science & Business Media

Published: 2013-09-07

Total Pages: 232

ISBN-13: 1461401615

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Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.

Kernels for Structured Data
Kernels for Structured Data

Author: Thomas G„rtner

Publisher: World Scientific

Published: 2008

Total Pages: 216

ISBN-13: 9812814558

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This book provides a unique treatment of an important area of machine learning and answers the question of how kernel methods can be applied to structured data. Kernel methods are a class of state-of-the-art learning algorithms that exhibit excellent learning results in several application domains. Originally, kernel methods were developed with data in mind that can easily be embedded in a Euclidean vector space. Much real-world data does not have this property but is inherently structured. An example of such data, often consulted in the book, is the (2D) graph structure of molecules formed by their atoms and bonds. The book guides the reader from the basics of kernel methods to advanced algorithms and kernel design for structured data. It is thus useful for readers who seek an entry point into the field as well as experienced researchers.

Learning Kernel Classifiers
Learning Kernel Classifiers

Author: Ralf Herbrich

Publisher: MIT Press

Published: 2022-11-01

Total Pages: 393

ISBN-13: 0262546590

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An overview of the theory and application of kernel classification methods. Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier—a limited, but well-established and comprehensively studied model—and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.

Regularization, Optimization, Kernels, and Support Vector Machines
Regularization, Optimization, Kernels, and Support Vector Machines

Author: Johan A.K. Suykens

Publisher: CRC Press

Published: 2014-10-23

Total Pages: 528

ISBN-13: 1482241390

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Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: Covers the relationship between support vector machines (SVMs) and the Lasso Discusses multi-layer SVMs Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing Describes graph-based regularization methods for single- and multi-task learning Considers regularized methods for dictionary learning and portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.