One theoretical motivation behind margin classifiers is that their generalization error may be bound by parameters of the algorithm and a margin term. An example of such a bound is for the AdaBoost algorithm[1].
Soft Margin Classifier
In real world problem it is not likely to get an exactly separate line dividing the data within the space. And we might have a curved decision boundary.
 See also: Machine learning, Classification, Percept, Support vector machine, Perceptron
  
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