'Support Vector Machine and its Applications in Classification Problems
A slecture by Xing Liu
Partially based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
Outline of the slecture
- Linear discriminant functions
- Summary
- References
Linear classification Problem Statement
In a linear classification problem, the feature space can be divided into different regions by hyperplanes. For a two-catagory case, given training data $ \vec{y}_1,\vec{y}_2,...\vec{y}_n \in \mathbb{R}^p $, with known class labels for each point $ w_1, w_2, ..., w_n \in \{+1,-1\} $, each $ \vec{y}_i $ is a p-dimensional vector. The goal is to find the maximum-margin hyperplane that separates the training sample points according to their class labels. The separation hyperplane can be written as $ c\cdot y=b $ where $ \cdot $ denotes the dot product, c determines the orientation of the hyperplane and