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An Introduction To Support Vector Machines And Other Kernel Based Learning Methods

Author: Nello Cristianini
Publisher: Cambridge University Press
ISBN: 9780521780193
Size: 16.83 MB
Format: PDF, Kindle
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This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.

Support Vector Machines Theory And Applications

Author: Lipo Wang
Publisher: Springer Science & Business Media
ISBN: 9783540243885
Size: 32.37 MB
Format: PDF, Kindle
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The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Machine Learning With Svm And Other Kernel Methods

Author: K.P. Soman
Publisher: PHI Learning Pvt. Ltd.
ISBN: 8120334353
Size: 59.77 MB
Format: PDF, ePub, Docs
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Support vector machines (SVMs) represent a breakthrough in the theory of learning systems. It is a new generation of learning algorithms based on recent advances in statistical learning theory. Designed for the undergraduate students of computer science and engineering, this book provides a comprehensive introduction to the state-of-the-art algorithm and techniques in this field. It covers most of the well known algorithms supplemented with code and data. One Class, Multiclass and hierarchical SVMs are included which will help the students to solve any pattern classification problems with ease and that too in Excel. KEY FEATURES  Extensive coverage of Lagrangian duality and iterative methods for optimization  Separate chapters on kernel based spectral clustering, text mining, and other applications in computational linguistics and speech processing  A chapter on latest sequential minimization algorithms and its modifications to do online learning  Step-by-step method of solving the SVM based classification problem in Excel.  Kernel versions of PCA, CCA and ICA The CD accompanying the book includes animations on solving SVM training problem in Microsoft EXCEL and by using SVMLight software . In addition, Matlab codes are given for all the formulations of SVM along with the data sets mentioned in the exercise section of each chapter.

Klassifikation Von Niederspannungsnetzen Mit Support Vector Machines Erzeugungsanlagen

Author: Sebastian Breker
Publisher: kassel university press GmbH
ISBN: 3737600147
Size: 72.40 MB
Format: PDF
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In dieser Arbeit werden verschiedene Fragestellungen zur Klassifikation von Niederspannungs-(NS)-Netzen hinsichtlich ihrer Aufnahmekapazität für dezentrale Erzeugungsanlagen (DEA) adressiert. Vor diesem Hintergrund werden effiziente Ansätze zur Bewertung von NS-Netzen vorgestellt. Die Anwendungsnähe und Einsatztauglichkeit der erarbeiteten Methoden wird konsequent durch praxisnahe Experimente an Daten einer Vielzahl realer NS-Netze unterstrichen. Ein Einsatz in der Praxis ist daher direkt möglich und kann z. B. zur Erhöhung der Planungssicherheit bei der Steuerung von Investitionsmitteln auf der NS-Ebene dienen. Weiterhin wird durch die Methoden eine strukturierte Möglichkeit zur Auswahl von relevanten NS-Netzstrukturen für detaillierte Untersuchungen geschaffen, indem z. B. von jeder Klasse eine bestimmte Anzahl an NS-Netzen für die Untersuchungen gewählt wird. Auf diese Weise kann der Anteil an „schwachen“ und „starken“ Netzen, die untersucht werden sollen, gesteuert werden, um repräsentative Ergebnisse über alle Klassen zu erhalten.

A Gentle Introduction To Support Vector Machines In Biomedicine

Author: Alexander Statnikov
Publisher: World Scientific Publishing Company
ISBN: 9813107995
Size: 28.97 MB
Format: PDF, ePub
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Support Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).

Learning With Kernels

Author: Bernhard Schölkopf
Publisher: MIT Press
ISBN: 9780262194754
Size: 15.39 MB
Format: PDF, ePub
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This volume provides an introduction to SVMs and related kernel methods. It provides concepts necessary to enable a reader to enter the world of machine learning using theoretical kernel algorithms and to understand and apply the algorithms that have been developed over the last few years.

Data Mining Algorithms

Author: Pawel Cichosz
Publisher: John Wiley & Sons
ISBN: 1118950801
Size: 48.76 MB
Format: PDF, ePub, Mobi
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Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

Advances In Large Margin Classifiers

Author: Alexander J. Smola
Publisher: MIT Press
ISBN: 9780262194488
Size: 33.89 MB
Format: PDF, Mobi
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The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms. The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.

Learning With Support Vector Machines

Author: Colin Campbell
Publisher: Morgan & Claypool Publishers
ISBN: 1608456161
Size: 64.51 MB
Format: PDF, Mobi
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Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector Machines for Classification / Kernel-based Models / Learning with Kernels

Kernel Based Data Fusion For Machine Learning

Author: Shi Yu
Publisher: Springer Science & Business Media
ISBN: 3642194052
Size: 72.67 MB
Format: PDF, ePub, Docs
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Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.