How is linear classifier relevant to SVM?

An svm is a type of linear classifier. If you don’t mess with kernels, it’s arguably the most simple type of linear classifier.

Linear classifiers (all?) learn linear fictions from your data that map your input to scores like so: scores = Wx + b. Where W is a matrix of learned weights, b is a learned bias vector that shifts your scores, and x is your input data. This type of function may look familiar to you if you remember y = mx + b from high school.

A typical svm loss function ( the function that tells you how good your calculated scores are in relation to the correct labels ) would be hinge loss. It takes the form: Loss = sum over all scores except the correct score of max(0, scores – scores(correct class) + 1).

In the context of machine learning interview questions, if you’re asked about how a linear classifier is relevant to Support Vector Machines (SVM), you can provide the following answer:

“A linear classifier is a type of algorithm that separates data points into different classes using a linear decision boundary. SVM, specifically, is a type of linear classifier that aims to find the optimal hyperplane to separate data points of different classes while maximizing the margin between the classes. The hyperplane is the decision boundary that best separates the data, and the margin is the distance between the hyperplane and the nearest data points of each class.

In summary, SVM is a type of linear classifier that not only classifies data but also focuses on finding the hyperplane that maximizes the margin, providing robustness and better generalization to unseen data. While linear classifiers, in general, classify data using linear decision boundaries, SVM takes it a step further by optimizing for the best possible separation between classes.”