public boolean trainWithFeatureInduction(InstanceList trainingData, InstanceList validationData, InstanceList testingData, TransducerEvaluator eval, int numIterations, int numIterationsBetweenFeatureInductions, int numFeatureInductions, int numFeaturesPerFeatureInduction, double trueLabelProbThreshold, boolean clusteredFeatureInduction, double[] trainingProportions) { return trainWithFeatureInduction(trainingData, validationData, testingData, eval, numIterations, numIterationsBetweenFeatureInductions, numFeatureInductions, numFeaturesPerFeatureInduction, trueLabelProbThreshold, clusteredFeatureInduction, trainingProportions, "exp"); }
crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 200, 0.5, true, new double[] {.1, .2, .5, .7}); else crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 1000, 0.5, false,
crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 200, 0.5, true, new double[] {.1, .2, .5, .7}); else crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 1000, 0.5, false,
crft.trainWithFeatureInduction(training, null, testing, eval, iterations, 10, 20, 500, 0.5, false, null); } else { boolean converged;
crft.trainWithFeatureInduction(training, null, testing, eval, iterations, 10, 20, 500, 0.5, false, null); } else { boolean converged;
crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 200, 0.5, true, new double[] {.1, .2, .5, .7}); else crft.trainWithFeatureInduction (trainingData, null, testingData, eval, 99999, 10, 99, 1000, 0.5, false,
crft.trainWithFeatureInduction(training, null, testing, eval, iterations, 10, 20, 500, 0.5, false, null); } else { boolean converged;
double[] trainingProportions) return trainWithFeatureInduction (trainingData, validationData, testingData, eval, numIterations, numIterationsBetweenFeatureInductions, numFeatureInductions, numFeaturesPerFeatureInduction,
double[] trainingProportions) return trainWithFeatureInduction (trainingData, validationData, testingData, eval, numIterations, numIterationsBetweenFeatureInductions, numFeatureInductions, numFeaturesPerFeatureInduction,