MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -3. Nonlinear classification: SVM can be extended to solve nonlinear classification tasks when the set of samples cannot be separated linearly. By applying kernel functions, the samples are mapped onto a high-dimensional feature space, in which the linear classification is possible.

Detailed explanation-2: -Non-linear SVM: Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as non-linear data and classifier used is called as Non-linear SVM classifier.

Detailed explanation-3: -Pros and Cons of SVM It is effective in high-dimensional spaces. It is effective in cases where the number of dimensions is greater than the number of samples. It uses a subset of the training set in the decision function (called support vectors), so it is also memory efficient.

Detailed explanation-4: -As mentioned above SVM is a linear classifier which learns an (n – 1)-dimensional classifier for classification of data into two classes. However, it can be used for classifying a non-linear dataset. This can be done by projecting the dataset into a higher dimension in which it is linearly separable!