15 edition of **Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)** found in the catalog.

- 346 Want to read
- 28 Currently reading

Published
**July 29, 2005**
by Springer
.

Written in English

- Pattern recognition,
- Machine learning,
- Computers - Desktop Publishing,
- Computers,
- Computer Books: General,
- Text processing (Computer science),
- Pattern recognition systems,
- Artificial Intelligence - General,
- Desktop Publishing - General,
- Engineering - Electrical & Electronic,
- Computers / Document Management,
- Fuzzy systems,
- Kernel methods,
- Neural networks,
- Pattern classification,
- Support vector machines,
- Text processing (Computer scie

The Physical Object | |
---|---|

Format | Hardcover |

Number of Pages | 357 |

ID Numbers | |

Open Library | OL8974488M |

ISBN 10 | 1852339292 |

ISBN 10 | 9781852339296 |

Support vector machines (SVMs), were originally formulated for two-class classification problems, and have been accepted as a powerful tool for developing pattern classification and . A novel twin parametric-margin support vector machine (TPMSVM) for classification is proposed in this paper. This TPMSVM, in the spirit of the twin support vector machine (TWSVM), determines indirectly the separating hyperplane through a pair of nonparallel parametric-margin hyperplanes solved by two smaller sized support vector machine (SVM.

"Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems. This book focuses on the application of support vector machines to pattern classification. Specifically, we discuss the properties of support vector machines that are useful for pattern classification applications, several multiclass models, and variants of support vector machines.

As with any supervised learning model, you first train a support vector machine, and then cross validate the classifier. Use the trained machine to classify (predict) new data. In addition, to obtain satisfactory predictive accuracy, you can use various SVM kernel functions, . The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are by:

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Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems.

Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various feature selection and extraction : Springer-Verlag London. Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation by: Support Vector Machines for Pattern Classification.

A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors.

Originally formulated for two-class classification problems, support vector machines (SVMs) are now accepted as powerful tools for developing pattern classification and function approximation systems.

Recent developments in kernel-based methods include kernel classifiers and regressors and their variants, advancements in generalization theory, and various Support Vector Machines for Pattern Classification book selection and extraction methods.

Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines.

To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers. Support vector machines (SVM) are a popular machine learning method to analyze data and recognize patterns.

An SVM performs classification by constructing an N -dimensional hyperplane (a plane generalized into N dimensions) that optimally separates the data into two categories. Keywords: Support Vector Machines, Statistical Learning Theory, VC Dimension, Pattern Recognition Appeared in: Data Mining and Knowledge Discovery 2,1.

Introduction The purpose of this paper is to provide an introductory yet extensive tutorial on the basic ideas behind Support Vector Machines (SVMs).

The books (Vapnik, The large margin distribution machine (LDM) combines the working principle of support vector machine (SVM) and the margin distribution to directly imp.

Support vector machines and their variants and extensions, often called kernel-based methods (or simply kernel methods), have been studied extensively and applied to various pattern classification Author: Shigeo Abe.

Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition) - Kindle edition by Abe, Shigeo. Download it once and read it on your Kindle device, PC, phones or tablets.

Use features like bookmarks, note taking and highlighting while reading Support Vector Machines for Pattern Classification (Advances in Computer Vision and Pattern Recognition).5/5(2). Support Vector Machines The goal of support vector machines (SVMs) is to find the optimal line (or hyperplane) that maximally separates the two classes.

(SVMs are used for binary classification, but can be extended to support multi-class classification). Mathematically, we can write the equation of that decision boundary as a line. Speci?cally, we discuss the properties of support vector machines that are useful for pattern classi?cation applications, several m- ticlass models, and variants of support vector machines.

To clarify their - plicability to real-world problems, we compare performance of most models discussed in the book using real-world benchmark data. Readers Brand: Springer-Verlag London. Twin Support Vector Machines for Pattern Classification Abstract: We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM.

The twin SVM formulation is in the spirit of proximal SVMs via generalized by: Support Vector Machines are a system for efficiently training the linear learning machines introduced in Chapter 2 in the kernel-induced feature spaces described in Chapter 3, while respecting the insights provided by the generalisation theory of Chapter 4, and exploiting the optimisation theory of Chapter by: Support vector machine - classifies data points by maximizing separating margin between two classes.

It can deal with both linear and nonlinearly separable datasets. where C can be viewed as Author: Shigeo Abe. Support Vector Machine is a generalization of maximal margin classifier. This classifier is simple, but it cannot be applied to the majority of the datasets since the classes must be separated by a boundary which is : Nagesh Singh Chauhan.

What equations are used for classification in a support vector machine. What is the output of SVM. Are support vector machines more prone to over-fitting compared to other algorithms given that curves can be drawn to fit around training data. Support Vector Machines for Pattern Classification Shigeo Abe Graduate School of Science and Technology Kobe University p = 1: L1 SVM, = 2: L2 SVM • Robust classification for outliers is possible by proper value selection of C.

Support Vector Machines for Pattern Classification book. Read reviews from world’s largest community for readers. Support vector machines are popular bec /5.

One of the most prevailing and exciting supervised learning models with associated learning algorithms that analyse data and recognise patterns is Support Vector Machines (SVMs). It is used for solving both regression and classification problems. The support vector machine (SVM) is a predictive analysis data-classification algorithm that assigns new data elements to one of labeled categories.

SVM is, in most cases, a binary classifier; it assumes that the data in question contains two possible target values. Another version of the SVM algorithm, multiclass SVM, augments SVM to be used as [ ].Support vector machines (SVMs) have gained wide acceptance due to its solid theoretical basis and high generalization ability for wide range of applications.

This tutorial emphasizes the applicability of SVMs to pattern classification problems. Three-layer neural networks are universal classifiers in that they can classify any.The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT\&T Bell Labs.

This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers.