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This chapter deals with a machine learning method termed as Support Vector Machines
(SVMs).
Introduction
Support vector machines (SVMs) are powerful yet flexible
supervised machine learning
methods used for classification, regression, and, outliers’ detection. SVMs are very efficient
in high dimensional spaces and generally are used in classification problems. SVMs are
popular and memory efficient because they use a subset of training points in the decision
function.
The main goal of SVMs is to divide the datasets into number of classes in order to find a
maximum marginal hyperplane (MMH)
which can be done in the following two steps:
Support Vector Machines will first generate hyperplanes iteratively that separates
the classes in the best way.
After that it will choose the hyperplane that segregate the classes correctly.
Some important concepts in SVM are as follows:
Support Vectors:
They may be defined as the datapoints which are closest to the
hyperplane. Support vectors help in deciding the separating line.
Hyperplane:
The decision plane or space that divides
set of objects having
different classes.
Margin:
The gap between two lines on the closet data points of different classes is
called margin.
Following diagrams will give you an insight about these SVM concepts:
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