Posted by **bookwyrm** at Feb. 6, 2015

2013 | 136 Pages | ISBN: 3319006320 | PDF | 5 MB

Posted by **tukotikko** at Jan. 25, 2014

2013 | 136 Pages | ISBN: 3319006320 | PDF | 5 MB

Posted by **AlenMiler** at Aug. 30, 2016

English | 25 Aug. 2016 | ISBN: 3319410628 | 112 Pages | PDF (True) | 1.78 MB

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.

Posted by **Nice_smile)** at Sept. 20, 2015

English | Dec. 15, 2001 | ISBN: 0262194759 | 644 Pages | PDF | 36.19 MB

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM).

Posted by **manamba13** at Feb. 10, 2015

English | 2005 | ISBN: 3540243887 | 431 Pages | PDF | 15 MB

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining.

Posted by **interes** at April 13, 2014

English | 2014 | ISBN: 3319022997 | 308 pages | PDF | 8,1 MB

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.

Posted by **ertugrul ergun** at March 23, 2007

Springer | ISBN: 3540243887 | 2005 Year | 431 Pages | PDF | 16 Mb

The support vector machine (SVM) has become one of the standard tools for machine learning and data mining. This carefully edited volume presents the state of the art of the mathematical foundation of SVM in statistical learning theory, as well as novel algorithms and applications. Support Vector Machines provides a selection of numerous real-world applications, such as bioinformatics, text categorization, pattern recognition, and object detection, written by leading experts in their respective fields.

Posted by **Alexpal** at Jan. 31, 2007

Publisher: The MIT Press; 1st edition (December 15, 2001) | ISBN-10: 0262194759 | PDF | 36,2 Mb | 644 pages

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.

Posted by **interes** at Dec. 5, 2015

English | 2012 | ISBN: 1848166915 | 320 pages | PDF | 9 MB

Posted by **leonardo78** at Nov. 1, 2016

Publisher: Morgan & Claypool | 2011 | ISBN: 1608456161 | 100 pages | PDF | 0,6 MB

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.