MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -This is the Maximum Margin Classifier. It maximizes the margin of the hyperplane. This is the best hyperplane because it reduces the generalization error the most. If we add new data, the Maximum Margin Classifier is the best hyperplane to correctly classify the new data.

Detailed explanation-2: -Due to the fact that the optimisation objective is to find the optimal hyperplane with maximum margin from closest support vectors, SVM models are also called as maximum margin classifier.

Detailed explanation-3: -Support Vector Classifier is an extension of the Maximal Margin Classifier. It is less sensitive to individual data. Since it allows certain data to be misclassified, it’s also known as the “Soft Margin Classifier”. It creates a budget under which the misclassification allowance is granted.

Detailed explanation-4: -The Maximal-Margin Classifier is a hypothetical classifier that best explains how SVM works in practice. The numeric input variables (x) in your data (the columns) form an n-dimensional space. For example, if you had two input variables, this would form a two-dimensional space.

Detailed explanation-5: -Support Vector Machine (SVM) is a type of “large margin” classifier, which seeks for a halfspace that separates a training set with a large margin, i.e. all the examples are not only on the correct side of the separating hyperplane but also far away from it.