Supervised Learning - Support Vector Machines

What is the purpose of Lagrange Multipliers in SVM optimization?

They are used to transform the constrained optimization problem into an unconstrained one, allowing the finding of optimal hyperplanes.

What do the Karush-Kuhn-Tucker (KKT) conditions ensure in SVM optimization?

They ensure that the optimal solution lies within the feasible region defined by the constraints of the SVM problem.

What is Maximum Margin Classification in the context of SVMs?

It refers to the principle of finding the hyperplane that maximizes the margin between different classes in the dataset.

Why is Maximum Margin Classification significant?

It helps to reduce overfitting and increases the generalization capability of the SVM model by maximizing the distance from the decision boundary to the nearest data points.

How is nonlinear SVM classification performed using inner product kernels?

By applying kernel functions, which allow the transformation of input space into a higher-dimensional feature space where the data points can be linearly separated.

What are common kernel functions used in SVMs?

Polynomial and Radial Basis Function (RBF) kernels.

What is the role of kernel functions in SVMs?

They enable the transformation of non-linearly separable data points into higher dimensions to facilitate linear separation.

What is the benefit of using RBF kernel in SVM classification?

It allows the model to adaptively fit the decision boundary to complex distributions of data.

How does transforming data into higher dimensions aid SVMs?

It makes it possible to find a hyperplane that separates classes that are not linearly separable in the original feature space.