Announcements  Project teams should be decided today! Otherwise, you will work alone.  If you have any question or uncertainty about the project, talk to me by appointment.  By tomorrow, you should have a working version of text classifier !

Support Vector Machine (III) Rong Jin

Recap: Support Vector Machine (SVM) for Noisy Data  Noisy data No perfect linear decision boundary  Balance the trade off between margin and classification errors denotes +1 denotes -1

Recap: Dual Problem  Original optimization problem for SVM  Parameters: W and b Vector W is in the feature space A weight parameter for each feature #parameters = # number of features May have too many parameters when the number of features is large  Text classification: 100,000 word features  100,000 parameters !

Recap: Dual Problem  Represent W as a linear combination of training examples  Parameter space: W  Every training example get a weight  i #parameter: #features  #training_examples  Why linear combination of training examples is sufficient?

Recap: Dual Problem Maximize where Subject to these constraints: Quadratic Programming For non-support-vector training data points:  i =0 Only support vectors have  i  0 Non single training example can be dominative Weights for positive and negative examples are balanced