kernel methods for pattern analysis

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Kernel Methods For Pattern Analysis

Author : John Shawe-Taylor
ISBN : 0521813972
Genre : Computers
File Size : 60. 60 MB
Format : PDF
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A unique account of developing topic in data mining and machine learning.

Kernel Methods For Pattern Analysis

Author : John Shawe-Taylor
ISBN : 9781139451611
Genre : Computers
File Size : 72. 88 MB
Format : PDF, Kindle
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Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

Kernel Methods And Machine Learning

Author : S. Y. Kung
ISBN : 9781107024960
Genre : Computers
File Size : 30. 3 MB
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Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Kernel Methods For Remote Sensing Data Analysis

Author : Gustau Camp-Valls
ISBN : 9780470749005
Genre : Technology & Engineering
File Size : 28. 77 MB
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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Kernel Methods In Computational Biology

Author : Bernhard Schölkopf
ISBN : 0262195097
Genre : Computers
File Size : 71. 78 MB
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A detailed overview of current research in kernel methods and their application to computational biology.

Digital Signal Processing With Kernel Methods

Author : José Luis Rojo-Ã?lvarez
ISBN : 9781118611791
Genre : Technology & Engineering
File Size : 80. 43 MB
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A realistic and comprehensive review of joint approaches to machine learning and signal processing algorithms, with application to communications, multimedia, and biomedical engineering systems Digital Signal Processing with Kernel Methods reviews the milestones in the mixing of classical digital signal processing models and advanced kernel machines statistical learning tools. It explains the fundamental concepts from both fields of machine learning and signal processing so that readers can quickly get up to speed in order to begin developing the concepts and application software in their own research. Digital Signal Processing with Kernel Methods provides a comprehensive overview of kernel methods in signal processing, without restriction to any application field. It also offers example applications and detailed benchmarking experiments with real and synthetic datasets throughout. Readers can find further worked examples with Matlab source code on a website developed by the authors. Presents the necessary basic ideas from both digital signal processing and machine learning concepts Reviews the state-of-the-art in SVM algorithms for classification and detection problems in the context of signal processing Surveys advances in kernel signal processing beyond SVM algorithms to present other highly relevant kernel methods for digital signal processing An excellent book for signal processing researchers and practitioners, Digital Signal Processing with Kernel Methods will also appeal to those involved in machine learning and pattern recognition.

Learning With Kernels

Author : Bernhard Schölkopf
ISBN : 0262194759
Genre : Computers
File Size : 58. 49 MB
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A comprehensive introduction to Support Vector Machines and related kernel methods.

Kernel Methods In Bioengineering Signal And Image Processing

Author : Camps-Valls, Gustavo
ISBN : 9781599040448
Genre : Technology & Engineering
File Size : 29. 43 MB
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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.

Pattern Analysis

Author : Heinrich Niemann
ISBN : 9783642966507
Genre : Computers
File Size : 85. 66 MB
Format : PDF
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This book is devoted to pattern analysis, that is, the automatic construc tion of a symbolic description for a complex pattern, like an image or con nected speech. Pattern analysis thus tries to simulate certain capabilities which go without saying in any human central nervous system. The increasing interest and growing efforts at solving the problems related with pattern analysis are motivated by the challenge of the problem and the expected ap plications. Potential applications are numerous and result from the fact that data can be gathered and stored by modern devices in ever increasing extent, thus making the finding of particular interesting facts or events in these hosts of data an ever increasing problem. It was tried to organize the book around one particular view of pattern analysis: the view that pattern analysis requires an appropriate set of modules operating on a common data base which contains interme processing diate results of processing. Although other views are certainly possible, this one was adopted because the author feels that it is a useful idea, be cause the size of this book had to be kept within reasonable bounds, and because it facilitated the composition of fairly self-contained chapters.

Kernel Methods In Computer Vision

Author : Christoph H. Lampert
ISBN : 9781601982681
Genre : Computers
File Size : 84. 86 MB
Format : PDF
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"Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection, and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency, and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature" --Abstract.

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