Question

Discuss the implications of an infinite-dimensional space in generalization ability of the hard-margin SVM. What are the strategies we can use to improve generalization

Answer 1

The SVM classifier's generalization performance is improved by decreasing the VC dimension.

What is SVM ?

SVM stands for Support Vector Machine .

A potent machine learning technique for classification and regression is called a Support vector machine. The selection of a margin type is crucial if we want to use it to solve an issue.

A soft margin or a hard margin can be chosen on the basis of the data , If the data is linearly separable , hard margin is used , If the data is not separable linearly and misclassifications can be allowed then soft margin is used.

The hard margin SVM's capacity for generalization is significantly influenced by VC.

the SVM classifier's generalization performance is improved by decreasing the VC dimension.

We contend that by using dimensionality reduction and bootstrapping techniques, the VC dimension of an SVM classifier can be decreased.

The SVM classifier's performance was now enhanced by bootstrapping both the original data and the projected data that is dimensionally reduced .

To know more about SVM

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