%{search_type} search results

• Introduction to support vector machines / Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India
• Journey of support vector machines : from maximum-margin hyperplane to a pair of non-parallel hyperplanes / Pooja Saigal, PhD, Vivekananda School of Information Technology, Vivekananda Institute of Professional Studies, New Delhi, India
• Power spectrum entropy-based support vector machine for quantitative diagnosis of rotor vibration process faults / Cheng-Wei Fei, Department of Aeronautics and Astronautics, Fudan University, Shanghai, China.

"Support Vector Machines: Evolution and Applications reviews the basics of Support Vector Machines (SVM), their evolution and applications in diverse fields. SVM is an efficient supervised learning approach popularly used for pattern recognition, medical image classification, face recognition and various other applications. In the last 25 years, a lot of research has been carried out to extend the use of SVM to a variety of domains. This book is an attempt to present the description of a conventional SVM, along with discussion of its different versions and recent application areas. The first chapter of this book introduces SVM and presents the optimization problems for a conventional SVM. Another chapter discusses the journey of SVM over a period of more than two decades. SVM is proposed as a separating hyperplane classifier that partitions the data belonging to two classes. Later on, various versions of SVM are proposed that obtain two hyperplanes instead of one. A few of these variants of SVM are discussed in this book. The major part of this book discusses some interesting applications of SVM in areas like quantitative diagnosis of rotor vibration process faults through power spectrum entropy-based SVM, hardware architectures of SVM applied in pattern recognition systems, speaker recognition using SVM, classification of iron ore in mines and simultaneous prediction of the density and viscosity for the ternary system water- ethanol-ethylene glycol ionic liquids. The latter part of the book is dedicated to various approaches for the extension of SVM and similar classifiers to a multi-category framework, so that they can be used for the classification of data with more than two classes"-- Provided by publisher

• Introduction.- Generalized Eigenvalue Proximal Support Vector Machines.- Twin Support Vector Machines (TWSVM) for Classification.- TWSVR: Twin Support Vector Machine Based Regression.- Variants of Twin Support Vector Machines: Some More Formulations.- TWSVM for Unsupervised and Semi-Supervised Learning.- Some Additional Topics.- Applications Based on TWSVM.- References.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which.

• Introduction
• Preliminaries
• Histogram Rule: Oracle Inequality and Learning Rates
• Localized SVMs: Oracle Inequalities and Learning Rates
• Discussion.

Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance.
(source: Nielsen Book Data)

• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
• (source: Nielsen Book Data)

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.
(source: Nielsen Book Data)

• Introduction
• Linear discriminant function
• Perceptron
• Linear support vector machines
• Kernel based SVM
• Application to social networks
• Conclusion.

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.

• Partial table of contents:
• THEORY OF LEARNING AND GENERALIZATION.
• Two Approaches to the Learning Problem.
• Estimation of the Probability Measure and Problem of Learning.
• Conditions for Consistency of Empirical Risk Minimization Principle.
• The Structural Risk Minimization Principle.
• Stochastic Ill--Posed Problems.
• SUPPORT VECTOR ESTIMATION OF FUNCTIONS.
• Perceptrons and Their Generalizations.
• SV Machines for Function Approximations, Regression Estimation, and Signal Processing.
• STATISTICAL FOUNDATION OF LEARNING THEORY.
• Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities.
• Necessary and Sufficient Conditions for Uniform One--Sided Convergence of Means to Their Expectations.
• Comments and Bibliographical Remarks.
• References.
• Index.
• (source: Nielsen Book Data)

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
(source: Nielsen Book Data)

• Preface
• 1. The learning methodology
• 2. Linear learning machines
• 3. Kernel-induced feature spaces
• 4. Generalisation theory
• 5. Optimisation theory
• 6. Support vector machines
• 7. Implementation techniques
• 8. Applications of support vector machines
• Appendix A: pseudocode for the SMO algorithm
• Appendix B: background mathematics
• Appendix C: glossary
• Appendix D: notation
• Bibliography
• Index.
• (source: Nielsen Book Data)

This is the first comprehensive introduction to Support Vector Machines (SVMs), a 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. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
(source: Nielsen Book Data)