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<font size="4">''''Support Vector Machine and its Applications in Classification Problems''' <br> </font> <font size="2">A [https://www.projectrhea.org/learning/slectures.php slecture] by Xing Liu</font> | <font size="4">''''Support Vector Machine and its Applications in Classification Problems''' <br> </font> <font size="2">A [https://www.projectrhea.org/learning/slectures.php slecture] by Xing Liu</font> | ||
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Partially based on the [[2014_Spring_ECE_662_Boutin|ECE662 Spring 2014 lecture]] material of [[user:mboutin|Prof. Mireille Boutin]]. | Partially based on the [[2014_Spring_ECE_662_Boutin|ECE662 Spring 2014 lecture]] material of [[user:mboutin|Prof. Mireille Boutin]]. | ||
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= Outline of the slecture = | = Outline of the slecture = | ||
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* Summary | * Summary | ||
* References | * References | ||
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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}^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. | + | 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.
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.
