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<math> g(\vec{y}) = c\cdot\vec{y}</math> | <math> g(\vec{y}) = c\cdot\vec{y}</math> | ||
| − | We want to find <math>c\in\mathbb{R}^{n+1}</math> so that a testing data point <math>\vec{y}_i</math> is labelled <math> { | + | We want to find <math>c\in\mathbb{R}^{n+1}</math> so that a testing data point <math>\vec{y}_i</math> is labelled <math> {w_1} </math> if <math> c\cdot\vec{y}>0</math> |
| + | <math> {w_2} </math> if <math> c\cdot\vec{y}<0</math> | ||
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Revision as of 11:02, 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.
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. In this lecture, we will take a two-catagory case to illustrate. Given training samples $ \vec{y}_1,\vec{y}_2,...\vec{y}_n \in \mathbb{R}^p $, each $ \vec{y}_i $ is a p-dimensional vector and belongs to either class $ w_1 $ or $ w_2 $. The goal is to find the maximum-margin hyperplane that separate the points in the feature space that belong to class $ w_1 $ from those belong to class$ w_2 $. The discriminate function can be written as
$ g(\vec{y}) = c\cdot\vec{y} $
We want to find $ c\in\mathbb{R}^{n+1} $ so that a testing data point $ \vec{y}_i $ is labelled $ {w_1} $ if $ c\cdot\vec{y}>0 $ $ {w_2} $ if $ c\cdot\vec{y}<0 $
. 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
