1. Support Vector MachinesBy
Roger Ballard
Tanqiuhao Chen

2. The Basic Method
Support vector machines are a type of supervised binary linear classifier
The idea behind support vector machines is to draw a hyperplane between two linearly separable groups of vectors
The hyperplane is drawn to maximize the distance from the hyperplane to the nearest vectors
These vectors are called the support vectors, giving the method its name
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3. Limitations of the Basic Method
Does not work if the data is not linearly separable
Can only be used to classify between two classes
Can only perform linear classification

4. Improvement: Working with Non-Linearly Separable Classes
Soft margin SVM
Hinge loss function
Penalize going over the line proportional to the distance over
Add a tuning parameter
Weights how important the correct side is compared to creating a large margin
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5. Improvement: Classification with More Than Two Classes
Create multiple binary SVMs and have a vote
Method 1: one vs all
N classifiers for class contains point or class doesn’t contain point
Most sure classifier wins
Method 2: one vs one
N2 classifiers: one for each pair of classes
Class that is voted for by the greatest number of classifiers wins
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6. Improvement: Performing Non-Linear Classification
The kernel trick
Map your data into a higher-dimensional space using some kernel
In this example, the radial basis kernel is used
Z value is Gaussian(radius from origin)
Perform linear classification in the higher- dimensional space
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