Class PerceptronTrainer

  • All Implemented Interfaces:

    public class PerceptronTrainer
    extends AbstractEventTrainer
    Trains models using the perceptron algorithm. Each outcome is represented as a binary perceptron classifier. This supports standard (integer) weighting as well average weighting as described in: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm. Michael Collins, EMNLP 2002.
    • Constructor Detail

      • PerceptronTrainer

        public PerceptronTrainer()
    • Method Detail

      • setTolerance

        public void setTolerance​(double tolerance)
        Specifies the tolerance. If the change in training set accuracy is less than this, stop iterating.
        tolerance -
      • setStepSizeDecrease

        public void setStepSizeDecrease​(double decrease)
        Enables and sets step size decrease. The step size is decreased every iteration by the specified value.
        decrease - - step size decrease in percent
      • setSkippedAveraging

        public void setSkippedAveraging​(boolean averaging)
        Enables skipped averaging, this flag changes the standard averaging to special averaging instead.

        If we are doing averaging, and the current iteration is one of the first 20 or it is a perfect square, then updated the summed parameters.

        The reason we don't take all of them is that the parameters change less toward the end of training, so they drown out the contributions of the more volatile early iterations. The use of perfect squares allows us to sample from successively farther apart iterations.

        averaging - averaging flag