Class BalancedWinnowTrainer

  • All Implemented Interfaces:

    public class BalancedWinnowTrainer
    extends ClassifierTrainer<BalancedWinnow>
    implements Boostable,
    An implementation of the training methods of a BalancedWinnow on-line classifier. Given a labeled instance (x, y) the algorithm computes dot(x, wi), for w1, ... , wc where wi is the weight vector for class i. The instance is classified as class j if the value of dot(x, wj) is the largest among the c dot products.

    The weight vectors are updated whenever the the classifier makes a mistake or just barely got the correct answer (highest dot product is within delta percent higher than the second highest). Suppose the classifier guessed j and answer was j'. For each feature i that is present, multiply w_ji by (1-epsilon) and multiply w_j'i by (1+epsilon)

    The above procedure is done multiple times to the training examples (default is 5), and epsilon is cut by the cooling rate at each iteration (default is cutting epsilon by half).

    Gary Huang
    See Also:
    Serialized Form
    • Constructor Detail

      • BalancedWinnowTrainer

        public BalancedWinnowTrainer()
        Default constructor. Sets all features to defaults.
      • BalancedWinnowTrainer

        public BalancedWinnowTrainer​(double epsilon,
                                     double delta,
                                     int maxIterations,
                                     double coolingRate)
        epsilon - percentage by which to increase/decrease weight vectors when an example is misclassified.
        delta - percentage by which the highest (and correct) dot product should exceed the second highest dot product before we consider an example to be correctly classified (margin width) when adjusting weights.
        maxIterations - maximum number of times to loop through training examples.
        coolingRate - percentage of epsilon to decrease after each iteration