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== Linear discriminant functions == | == Linear discriminant functions == | ||
| − | In a linear classification problem, the feature space can be divided into different regions by hyperplanes. Given training data <math> \vec{x}_1,\vec{x}_2,...\vec{x}_n \in \mathbb{R}^ | + | In a linear classification problem, the feature space can be divided into different regions by hyperplanes. Given training data <math> \vec{x}_1,\vec{x}_2,...\vec{x}_n \in \mathbb{R}^p</math>, with known class labels for each point <math>y_1, y_2, ..., y_n \in {+1,-1}</math>, each <math> \vec{x}_i </math> is a p-dimensional vector. |
Revision as of 09:47, 1 May 2014
'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.
NOTE FROM INSTRUCTOR: I DO NOT COVER THIS TOPIC IN MY LECTURES. YOUR SLECTURE IS SUPPOSED TO BE BASED ON MY TEACHING MATERIAL. -PM
Outline of the slecture
- Linear discriminant functions
- Summary
- References
Linear discriminant functions
In a linear classification problem, the feature space can be divided into different regions by hyperplanes. Given training data $ \vec{x}_1,\vec{x}_2,...\vec{x}_n \in \mathbb{R}^p $, with known class labels for each point $ y_1, y_2, ..., y_n \in {+1,-1} $, each $ \vec{x}_i $ is a p-dimensional vector.
