1 edition of Learning with support vector machines found in the catalog.
Learning with support vector machines
2011 by Morgan & Claypool in San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) .
Written in English
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 handwritten 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.
|Other titles||Synthesis digital library of engineering and computer science.|
|Statement||Colin Campbell, Yiming Ying|
|Series||Synthesis lectures on artificial intelligence and machine learning -- # 10|
|LC Classifications||Q325.5 .C255 2011|
|The Physical Object|
|Format||[electronic resource] /|
|ISBN 10||9781608456178, 9781608456161|
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Support vector machine is another simple algorithm that every machine learning expert should have in his/her arsenal. Support vector machine is highly preferred by many as it produces significant accuracy with less computation power.
Support Vector Machine, abbreviated as SVM can be used for both regression and classification tasks. But, it is Author: Rohith Gandhi. * Gunn, Support Vector Machines for Classification and Regression, * Hearst et al., Intro to SVM: In the s, 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. 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 Cited by: This book is a nice and, I would say, a successful attempt to provide a unified survey of important theoretical and practical machine learning tools: neural networks (NN), support vector machines (SVM) and fuzzy systems (FS).Cited by: Book Abstract: In the s, 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. This textbook provides a thorough introduction to the field of learning from experimental data and soft computing.
Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but.
Support Vector Machines (SVM) have been recently developed in the framework of statistical learning theory, and have been successfully applied to a number of applications, ranging from time series.
Żbikowski, K. (): “ Using Volume Weighted Support Vector Machines with Walk Forward Testing and Feature Selection for the Purpose of Creating Stock Trading Strategy.
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