Machine Learning: Lecture Notes (Week 7)

Support Vector Machine (SVM)
The cost function will be modified compared to the sigmod function. It will consist of a constant slope session and a constant session.

In SVM, the parameters CA+B shall be minimized. A is the cost term, B is the regularization term and C controls the weight between them.

When training the hypothesis function, the hypothesis (theta multiplied by input data) shall be

• Cost1: Bigger than 1, if y = 1
• Cost0: Smaller than -1, if y = 1

Support Vector Machine
Define f as a similarity function that calculates the proximity to landmarks (combinations of feature values):

The function is based on a Gaussian kernel. Sigma affects the pointyness of the curve - a small sigma means a pointier curve.

Big C (small lambda): Low bias, high variance. Optimizing training set.
Small C (big lambda): High bias, low variance. Optimizing to reduce overfitting.