An Introduction To Support Vector Machines PDF Download
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Author | : Nello Cristianini |
Publisher | : Cambridge University Press |
Total Pages | : 216 |
Release | : 2000-03-23 |
Genre | : Computers |
ISBN | : 9780521780193 |
Download An Introduction to Support Vector Machines and Other Kernel-based Learning Methods Book in PDF, ePub and Kindle
This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.
Author | : Ingo Steinwart |
Publisher | : Springer Science & Business Media |
Total Pages | : 611 |
Release | : 2008-09-15 |
Genre | : Computers |
ISBN | : 0387772421 |
Download Support Vector Machines Book in PDF, ePub and Kindle
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.
Author | : Bernhard Scholkopf |
Publisher | : MIT Press |
Total Pages | : 645 |
Release | : 2018-06-05 |
Genre | : Computers |
ISBN | : 0262536579 |
Download Learning with Kernels Book in PDF, ePub and Kindle
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.
Author | : Alexander Statnikov |
Publisher | : World Scientific |
Total Pages | : 200 |
Release | : 2011 |
Genre | : Computers |
ISBN | : 9814324388 |
Download A Gentle Introduction to Support Vector Machines in Biomedicine: Theory and methods Book in PDF, ePub and Kindle
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 cases studies (Volume 2).The proposed book follows the approach of ?programmed learning? whereby material is presented in short sections called ?frames?. Each frame consists of a very small amount of information to be learned, a multiple choice quiz, and answers to the quiz. The reader can proceed to the next frame only after verifying the correct answers to the current frame.
Author | : Thorsten Joachims |
Publisher | : Springer Science & Business Media |
Total Pages | : 218 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 1461509076 |
Download Learning to Classify Text Using Support Vector Machines Book in PDF, ePub and Kindle
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 | : Luis Serrano |
Publisher | : Simon and Schuster |
Total Pages | : 510 |
Release | : 2021-12-14 |
Genre | : Computers |
ISBN | : 1617295914 |
Download Grokking Machine Learning Book in PDF, ePub and Kindle
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.
Author | : Lutz H. Hamel |
Publisher | : John Wiley & Sons |
Total Pages | : 211 |
Release | : 2011-09-20 |
Genre | : Computers |
ISBN | : 1118211030 |
Download Knowledge Discovery with Support Vector Machines Book in PDF, ePub and Kindle
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.
Author | : Shigeo Abe |
Publisher | : Springer Science & Business Media |
Total Pages | : 332 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 1447102851 |
Download Pattern Classification Book in PDF, ePub and Kindle
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 | : Naiyang Deng |
Publisher | : CRC Press |
Total Pages | : 345 |
Release | : 2012-12-17 |
Genre | : Business & Economics |
ISBN | : 1439857938 |
Download Support Vector Machines Book in PDF, ePub and Kindle
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
Author | : M.N. Murty |
Publisher | : Springer |
Total Pages | : 103 |
Release | : 2016-08-16 |
Genre | : Computers |
ISBN | : 3319410636 |
Download Support Vector Machines and Perceptrons Book in PDF, ePub and Kindle
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.>