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7 - Binary Classification with Support Vector Machines

Published online by Cambridge University Press:  05 December 2012

By
Patrick Nichols ,
Bobbie-Jo Webb-Robertson  and
Christopher Oehmen
Edited by
Ian Gorton  and
Deborah K. Gracio
Patrick Nichols
Affiliation:
Pacific Northwest National Laboratory
Bobbie-Jo Webb-Robertson
Affiliation:
Pacific Northwest National Laboratory
Christopher Oehmen
Affiliation:
Pacific Northwest National Laboratory
Ian Gorton
Affiliation:
Pacific Northwest National Laboratory, Washington
Deborah K. Gracio
Affiliation:
Pacific Northwest National Laboratory, Washington
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Summary

Introduction

Support vector machines (SVM) are currently one of the most popular and accurate methods for binary data classification and prediction. They have been applied to a variety of data and situations such as cyber-security, bioinformatics, web searches, medical risk assessment, financial analysis, and other areas [1]. This type of machine learning is shown to be accurate and is able to generalize predictions based upon previously learned patterns. However, current implementations are limited in that they can only be trained accurately on examples numbering to the tens of thousands and usually run only on serial computers. There are exceptions. A prime example is the annual machine learning and classification competitions such as the International Conference on Artificial Neural Networks (ICANN), which present problems with more than 100,000 elements to be classified. However, in order to treat such large test cases the formalism of the support vector machines must be modified.

SVMs were first developed by Vapnik and collaborators [2] as an extension to neural networks. Assume that we can convert the data values associated with an entity into numerical values that form a vector in the mathematical sense. These vectors form a space. Also, assume that this space of vectors can be separated by a hyperplane into the vectors that belong to one class and those that form the opposing class.

Type
Chapter
Information
Data-Intensive Computing
Architectures, Algorithms, and Applications
 , pp. 157 - 179
Publisher: Cambridge University Press
Print publication year: 2012

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References

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