Support Vector Machines For Pattern Classification
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| Author | : Shigeo Abe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 332 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1447102851 |
This book provides a unified approach for developing a fuzzy classifier and explains the advantages and disadvantages of different classifiers through extensive performance evaluation of real data sets. It thus offers new learning paradigms for analyzing neural networks and fuzzy systems, while training fuzzy classifiers. Function approximation is also treated and function approximators are compared.
| Author | : Shigeo Abe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 362 |
| Release | : 2005-07-29 |
| Genre | : Computers |
| ISBN | : 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 function approximations systems. This book provides a unique perspective of the state of the art in SVMs by taking the only approach that focuses on classification rather than covering the theoretical aspects. The book clarifies the characteristics of two-class SVMs through their extensive analysis, presents various useful architectures for multiclass classification and function approximation problems, and discusses kernel methods for improving generalization ability of conventional neural networks and fuzzy systems. Ample illustrations, examples and computer experiments are included to help readers understand the new ideas and their usefulness. This book supplies a comprehensive resource for the use of SVMs in pattern classification and will be invaluable reading for researchers, developers & students in academia and industry.
| Author | : Seong-Whan Lee |
| Publisher | : Springer |
| Total Pages | : 433 |
| Release | : 2003-08-02 |
| Genre | : Computers |
| ISBN | : 3540456651 |
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.
| Author | : Jayadeva |
| Publisher | : Springer |
| Total Pages | : 221 |
| Release | : 2016-10-12 |
| Genre | : Technology & Engineering |
| ISBN | : 3319461869 |
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.
| Author | : Thorsten Joachims |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 218 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461509076 |
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.
| Author | : Shigeo Abe |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 486 |
| Release | : 2010-07-23 |
| Genre | : Technology & Engineering |
| ISBN | : 1849960984 |
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. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.
| Author | : Nello Cristianini |
| Publisher | : Cambridge University Press |
| Total Pages | : 216 |
| Release | : 2000-03-23 |
| Genre | : Computers |
| ISBN | : 9780521780193 |
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
| Author | : Yunqian Ma |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 306 |
| Release | : 2014-02-12 |
| Genre | : Technology & Engineering |
| ISBN | : 3319023004 |
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.
| Author | : M.N. Murty |
| Publisher | : Springer |
| Total Pages | : 103 |
| Release | : 2016-08-16 |
| Genre | : Computers |
| ISBN | : 3319410636 |
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.>
| Author | : Naiyang Deng |
| Publisher | : CRC Press |
| Total Pages | : 345 |
| Release | : 2012-12-17 |
| Genre | : Business & Economics |
| ISBN | : 1439857938 |
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
