Examples of cc.mallet.classify.BalancedWinnowTrainer

cc.mallet.classify.BalancedWinnowTrainer

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). @author Gary Huang ghuang@cs.umass.edu

    
    public TokenClassifiers(InstanceList trainList, int randSeed, int numCV)
    {
//      this(new AdaBoostM2Trainer(new DecisionTreeTrainer(2), 10), trainList, randSeed, numCV);
//      this(new NaiveBayesTrainer(), trainList, randSeed, numCV);
      this(new BalancedWinnowTrainer(), trainList, randSeed, numCV);
//      this(new SVMTrainer(), trainList, randSeed, numCV);
    }

Examples of cc.mallet.classify.BalancedWinnowTrainer

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