Learning With Support Vector Machines

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Support Vector Machines

Ingo Steinwart,Andreas Christmann
Support Vector Machines Book PDF

Author : Ingo Steinwart,Andreas Christmann
Publisher : Springer Science & Business Media
Page : 611 pages
File Size : 47,9 Mb
Release : 2008-09-15
Category : Computers
ISBN : 9780387772424

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Support Vector Machines by Ingo Steinwart,Andreas Christmann Pdf

Every mathematical discipline goes through three periods of development: the naive, the formal, and the critical. David Hilbert The goal of this book is to explain the principles that made support vector machines (SVMs) a successful modeling and prediction tool for a variety of applications. We try to achieve this by presenting the basic ideas of SVMs together with the latest developments and current research questions in a uni?ed style. In a nutshell, we identify at least three reasons for the success of SVMs: their ability to learn well with only a very small number of free parameters, their robustness against several types of model violations and outliers, and last but not least their computational e?ciency compared with several other methods. Although there are several roots and precursors of SVMs, these methods gained particular momentum during the last 15 years since Vapnik (1995, 1998) published his well-known textbooks on statistical learning theory with aspecialemphasisonsupportvectormachines. Sincethen,the?eldofmachine learninghaswitnessedintenseactivityinthestudyofSVMs,whichhasspread moreandmoretootherdisciplinessuchasstatisticsandmathematics. Thusit seems fair to say that several communities are currently working on support vector machines and on related kernel-based methods. Although there are many interactions between these communities, we think that there is still roomforadditionalfruitfulinteractionandwouldbegladifthistextbookwere found helpful in stimulating further research. Many of the results presented in this book have previously been scattered in the journal literature or are still under review. As a consequence, these results have been accessible only to a relativelysmallnumberofspecialists,sometimesprobablyonlytopeoplefrom one community but not the others.

Learning with Support Vector Machines

Colin Pigozzi,Yiming Genesereth
Learning with Support Vector Machines Book PDF

Author : Colin Pigozzi,Yiming Genesereth
Publisher : Springer Nature
Page : 83 pages
File Size : 46,6 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031015526

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Learning with Support Vector Machines by Colin Pigozzi,Yiming Genesereth Pdf

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

Learning with Kernels

Bernhard Scholkopf,Alexander J. Smola
Learning with Kernels Book PDF

Author : Bernhard Scholkopf,Alexander J. Smola
Publisher : MIT Press
Page : 645 pages
File Size : 55,6 Mb
Release : 2018-06-05
Category : Computers
ISBN : 9780262536578

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Learning with Kernels by Bernhard Scholkopf,Alexander J. Smola Pdf

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs—-kernels—for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics. Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

Learning to Classify Text Using Support Vector Machines

Thorsten Joachims
Learning to Classify Text Using Support Vector Machines Book PDF

Author : Thorsten Joachims
Publisher : Springer Science & Business Media
Page : 218 pages
File Size : 42,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461509073

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Learning to Classify Text Using Support Vector Machines by Thorsten Joachims Pdf

Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning.

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

Nello Cristianini,John Shawe-Taylor
An Introduction to Support Vector Machines and Other Kernel based Learning Methods Book PDF

Author : Nello Cristianini,John Shawe-Taylor
Publisher : Cambridge University Press
Page : 216 pages
File Size : 52,7 Mb
Release : 2000-03-23
Category : Computers
ISBN : 0521780195

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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini,John Shawe-Taylor Pdf

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

Knowledge Discovery with Support Vector Machines

Lutz H. Hamel
Knowledge Discovery with Support Vector Machines Book PDF

Author : Lutz H. Hamel
Publisher : John Wiley & Sons
Page : 211 pages
File Size : 48,7 Mb
Release : 2011-09-20
Category : Computers
ISBN : 9781118211038

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Knowledge Discovery with Support Vector Machines by Lutz H. Hamel Pdf

An easy-to-follow introduction to support vector machines This book provides an in-depth, easy-to-follow introduction to support vector machines drawing only from minimal, carefully motivated technical and mathematical background material. It begins with a cohesive discussion of machine learning and goes on to cover: Knowledge discovery environments Describing data mathematically Linear decision surfaces and functions Perceptron learning Maximum margin classifiers Support vector machines Elements of statistical learning theory Multi-class classification Regression with support vector machines Novelty detection Complemented with hands-on exercises, algorithm descriptions, and data sets, Knowledge Discovery with Support Vector Machines is an invaluable textbook for advanced undergraduate and graduate courses. It is also an excellent tutorial on support vector machines for professionals who are pursuing research in machine learning and related areas.

Support Vector Machines and Perceptrons

M.N. Murty,Rashmi Raghava
Support Vector Machines and Perceptrons Book PDF

Author : M.N. Murty,Rashmi Raghava
Publisher : Springer
Page : 103 pages
File Size : 50,8 Mb
Release : 2016-08-16
Category : Computers
ISBN : 9783319410630

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Support Vector Machines and Perceptrons by M.N. Murty,Rashmi Raghava Pdf

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.>

Support Vector Machines Applications

Yunqian Ma,Guodong Guo
Support Vector Machines Applications Book PDF

Author : Yunqian Ma,Guodong Guo
Publisher : Springer Science & Business Media
Page : 306 pages
File Size : 44,8 Mb
Release : 2014-02-12
Category : Technology & Engineering
ISBN : 9783319023007

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Support Vector Machines Applications by Yunqian Ma,Guodong Guo Pdf

Support vector machines (SVM) have both a solid mathematical background and practical applications. This book focuses on the recent advances and applications of the SVM, such as image processing, medical practice, computer vision, and pattern recognition, machine learning, applied statistics, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications.

Grokking Machine Learning

Luis Serrano
Grokking Machine Learning Book PDF

Author : Luis Serrano
Publisher : Simon and Schuster
Page : 510 pages
File Size : 51,5 Mb
Release : 2021-12-14
Category : Computers
ISBN : 9781617295911

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Grokking Machine Learning by Luis Serrano Pdf

Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Pattern Recognition with Support Vector Machines

Seong-Whan Lee,Alessandro Verri
Pattern Recognition with Support Vector Machines Book PDF

Author : Seong-Whan Lee,Alessandro Verri
Publisher : Springer Science & Business Media
Page : 433 pages
File Size : 50,7 Mb
Release : 2002-07-29
Category : Computers
ISBN : 9783540440161

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Pattern Recognition with Support Vector Machines by Seong-Whan Lee,Alessandro Verri Pdf

This book constitutes the refereed proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines, SVM 2002, held in Niagara Falls, Canada in August 2002. The 16 revised full papers and 14 poster papers presented together with two invited contributions were carefully reviewed and selected from 57 full paper submissions. The papers presented span the whole range of topics in pattern recognition with support vector machines from computational theories to implementations and applications.

Imbalanced Learning

Haibo He,Yunqian Ma
Imbalanced Learning Book PDF

Author : Haibo He,Yunqian Ma
Publisher : John Wiley & Sons
Page : 222 pages
File Size : 45,6 Mb
Release : 2013-06-07
Category : Technology & Engineering
ISBN : 9781118646335

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Imbalanced Learning by Haibo He,Yunqian Ma Pdf

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on: Foundations of Imbalanced Learning Imbalanced Datasets: From Sampling to Classifiers Ensemble Methods for Class Imbalance Learning Class Imbalance Learning Methods for Support Vector Machines Class Imbalance and Active Learning Nonstationary Stream Data Learning with Imbalanced Class Distribution Assessment Metrics for Imbalanced Learning Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.

Machine Learning and Its Applications

Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos
Machine Learning and Its Applications Book PDF

Author : Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos
Publisher : Springer
Page : 324 pages
File Size : 52,7 Mb
Release : 2003-06-29
Category : Computers
ISBN : 9783540446736

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Machine Learning and Its Applications by Georgios Paliouras,Vangelis Karkaletsis,Constantine D. Spyropoulos Pdf

In recent years machine learning has made its way from artificial intelligence into areas of administration, commerce, and industry. Data mining is perhaps the most widely known demonstration of this migration, complemented by less publicized applications of machine learning like adaptive systems in industry, financial prediction, medical diagnosis and the construction of user profiles for Web browsers. This book presents the capabilities of machine learning methods and ideas on how these methods could be used to solve real-world problems. The first ten chapters assess the current state of the art of machine learning, from symbolic concept learning and conceptual clustering to case-based reasoning, neural networks, and genetic algorithms. The second part introduces the reader to innovative applications of ML techniques in fields such as data mining, knowledge discovery, human language technology, user modeling, data analysis, discovery science, agent technology, finance, etc.

Linear and Nonlinear Optimization

Igor Griva,Stephen G. Nash,Ariela Sofer
Linear and Nonlinear Optimization Book PDF

Author : Igor Griva,Stephen G. Nash,Ariela Sofer
Publisher : SIAM
Page : 742 pages
File Size : 40,8 Mb
Release : 2009-03-26
Category : Mathematics
ISBN : 9780898716610

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Linear and Nonlinear Optimization by Igor Griva,Stephen G. Nash,Ariela Sofer Pdf

Flexible graduate textbook that introduces the applications, theory, and algorithms of linear and nonlinear optimization in a clear succinct style, supported by numerous examples and exercises. It introduces important realistic applications and explains how optimization can address them.

Regularization, Optimization, Kernels, and Support Vector Machines

Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Regularization Optimization Kernels and Support Vector Machines Book PDF

Author : Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou
Publisher : CRC Press
Page : 528 pages
File Size : 43,9 Mb
Release : 2014-10-23
Category : Computers
ISBN : 9781482241396

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Regularization, Optimization, Kernels, and Support Vector Machines by Johan A.K. Suykens,Marco Signoretto,Andreas Argyriou Pdf

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.

Twin Support Vector Machines

Jayadeva,Reshma Khemchandani,Suresh Chandra
Twin Support Vector Machines Book PDF

Author : Jayadeva,Reshma Khemchandani,Suresh Chandra
Publisher : Springer
Page : 211 pages
File Size : 51,9 Mb
Release : 2016-10-12
Category : Technology & Engineering
ISBN : 9783319461861

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Twin Support Vector Machines by Jayadeva,Reshma Khemchandani,Suresh Chandra Pdf

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.