List of train() Examples

Examples of train()

aima.core.learning.framework.Learner.train()
aima.core.learning.learners.AdaBoostLearner.train()
aima.core.learning.learners.CurrentBestLearner.train()
aima.core.learning.learners.DecisionListLearner.train()
Induces the decision list from the specified set of examples @param ds a set of examples for constructing the decision list
aima.core.learning.learners.DecisionTreeLearner.train()
Induces the decision tree from the specified set of examples @param ds a set of examples for constructing the decision tree
aima.core.learning.learners.MajorityLearner.train()
cc.mallet.fst.CRFTrainerByLabelLikelihood.train()
cc.mallet.fst.CRFTrainerByStochasticGradient.train()
cc.mallet.fst.CRFTrainerByThreadedLabelLikelihood.train()
cc.mallet.fst.MEMMTrainer.train()
Trains a MEMM until convergence.
cc.mallet.fst.semi_supervised.CRFTrainerByGE.train()
cc.mallet.fst.semi_supervised.pr.CRFTrainerByPR.train()
cc.mallet.grmm.learning.ACRFTrainer.train()
cc.mallet.grmm.learning.DefaultAcrfTrainer.train()
com.aliasi.classify.DynamicLMClassifier.train()
com.clearnlp.classification.algorithm.AbstractAdaGrad.train()
com.clearnlp.component.tagger.EnglishOnlinePOSTagger.train()
com.clearnlp.nlp.train.AbstractNLPTrainer.train()
com.github.neuralnetworks.training.DNNLayerTrainer.train()
com.github.neuralnetworks.training.backpropagation.BackPropagationAutoencoder.train()
com.github.neuralnetworks.training.rbm.AparapiCDTrainer.train()
com.github.neuralnetworks.training.rbm.DBNTrainer.train()
com.jgaap.classifiers.NearestNeighborDriver.train()
com.jgaap.generics.AnalysisDriver.train()
Pass in samples of known author's writing Generate the models that unknown documents will be tested against @param knownEventSets Samples of known authors writing
com.rapidminer.RatingPrediction.UserItemBaseline.Train()
cz.zcu.fav.liks.ml.featuresel.filtering.BasicFilterTrainer.train()
cz.zcu.fav.liks.ml.featuresel.filtering.FilterTrainer.train()
Extracts probibalites from feature vector list. Should allways be called first. @param tfvl feature vector list
etc.aloe.processes.Training.train()
Given a set of labeled examples, generates a trained model. @param examples @return
fr.lip6.jkernelmachines.classifier.DoublePegasosSVM.train()
fr.lip6.jkernelmachines.classifier.DoubleQNPKL.train()
fr.lip6.jkernelmachines.classifier.DoubleSAG.train()
fr.lip6.jkernelmachines.classifier.transductive.S3VMLightPegasos.train()
fr.lip6.jkernelmachines.density.DoubleGaussianMixtureModel.train()
fr.lip6.jkernelmachines.projection.DoublePCA.train()
Train the projectors on a given data-set. @param list the list of training samples
gannuCF.classifiers.Classifier.train()
Method for training the classifier without including the corresponding samples. It uses the generated frequency vector. So, use this method when you want to control the generation of the Word Space Model. @param classes Number of target classes. @param sampleCount Number of samples to be loaded. @param attributeCount Number of attributes in the Word Space Model. @param samples The frequency matrix for training the classifier. Use the first dimension for controlling the sample number and the second dimension for controlling the Word Space Model (attribute number). I.E.: samples[2][1] will contain the weight of the second word on the third sample. The class is included as the last attribute of each sample.
id3.ID3Inducer.train()
opennlp.tools.ml.EventModelSequenceTrainer.train()
opennlp.tools.ml.EventTrainer.train()
opennlp.tools.ml.SequenceTrainer.train()
org.apache.ctakes.relationextractor.eval.ModifierExtractorEvaluation.train()
org.apache.ctakes.relationextractor.eval.RelationExtractorEvaluation.train()
org.apache.hama.ml.ann.SmallLayeredNeuralNetwork.train()
org.apache.hama.ml.recommendation.cf.OnlineCF.train()
{@inheritDoc}
org.apache.mahout.classifier.sgd.AdaptiveLogisticRegression.train()
org.apache.mahout.classifier.sgd.OnlineLogisticRegression.train()
org.apache.mahout.clustering.classify.ClusterClassifier.train()
org.data2semantics.proppred.predictors.SVMPropertyPredictor.train()
org.encog.ensemble.GenericEnsembleML.train()
org.fnlp.app.tc.TextClassifier.train()
训练 @param reader @param modelFile @throws Exception
org.fnlp.ml.classifier.bayes.BayesTrainer.train()
org.fnlp.ml.classifier.hier.PATrainer.train()
训练 @param eval
org.fnlp.ml.classifier.linear.OnlineTrainer.train()
参数训练方法 @return 线性分类器
org.fnlp.nlp.parser.dep.train.JointParerTrainer.train()
模型训练函数 @param dataFile 训练文件 @param maxite 最大迭代次数 @throws IOException @throws Exception
org.fnlp.train.pos.POSTrain.train()
org.jasen.core.engine.JasenTrainer.train()
Trains the engine to produce the JasenMap @throws JasenException @see JasenMap
org.mitre.jcarafe.jarafe.JarafeMETrainer.train()
resys.impl.svdplusplus.SVDPlusPlusRecommender.train()
Main training procedure
seekfeel.miners.supervised.SupervisedPanel.train()
tv.floe.metronome.classification.neuralnetworks.networks.MultiLayerPerceptronNetwork.train()
tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression.train()
zdenekdrahos.AI.KohonenMap.IKohonenMap.train()
zdenekdrahos.AI.KohonenMap.KohonenMap.train()
zdenekdrahos.AI.Training.ITraining.train()

Examples of com.rapidminer.RatingPrediction.UserItemBaseline.Train()

         recommendAlg.SetMinRating(getParameterAsInt("Min Rating"));
         recommendAlg.SetMaxRating(recommendAlg.GetMinRating()+getParameterAsInt("Range"));
         
         recommendAlg.SetRatings(training_data);


         recommendAlg.Train();
         
        exampleSetOutput.deliver(exampleSet);
        exampleSetOutput1.deliver(recommendAlg);
        }
  }

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Examples of cz.zcu.fav.liks.ml.featuresel.filtering.BasicFilterTrainer.train()

    extractor = new FeatureExtractor<Review>(set);
    TrainingFeatureVectorList tfvl = extractor.extractTrainingFeatures(instances);


    if (filter != null) {
      FilterTrainer filterTrainer = new BasicFilterTrainer();
      filterTrainer.train(tfvl);
      tfvlReduction += filter.filter(tfvl, minWeight, filterTrainer);
    }


    classifier = trainer.train(tfvl);

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Examples of cz.zcu.fav.liks.ml.featuresel.filtering.FilterTrainer.train()

    extractor = new FeatureExtractor<Review>(set);
    TrainingFeatureVectorList tfvl = extractor.extractTrainingFeatures(instances);


    if (filter != null) {
      FilterTrainer filterTrainer = new BasicFilterTrainer();
      filterTrainer.train(tfvl);
      tfvlReduction += filter.filter(tfvl, minWeight, filterTrainer);
    }


    classifier = trainer.train(tfvl);

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Examples of etc.aloe.processes.Training.train()

        ExampleSet examples = extraction.extractFeatures(basicExamples, this.featureSpecification);


        //Train the model
        Training training = getTrainingImpl();
        this.featureValues = examples.getInstances();
        this.model = training.train(examples);


        //Get the top features
        this.topFeatures = getFeatureWeightingImpl().getTopFeatures(examples, this.model, NUM_TOP_FEATURES);
        this.featureWeights = getFeatureWeightingImpl().getFeatureWeights(examples, this.model);
    }

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Examples of fr.lip6.jkernelmachines.classifier.DoublePegasosSVM.train()


      svm.setLambda(1. / (train.size() * Double
          .parseDouble(regularizationField.getText())));
      svm.setK(train.size() / 20);
      svm.setT(10 * train.size());
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      svLabel.setText("N/A");

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Examples of fr.lip6.jkernelmachines.classifier.DoublePegasosSVM.train()

      }
      for (int d = 1; d < 5; d++) {
        svm.addKernel(new DoublePolynomial(d));
        svm.addKernel(new DoubleHPolynomial(d));
      }
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;

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Examples of fr.lip6.jkernelmachines.classifier.DoublePegasosSVM.train()

        .toString())) {
      DoubleQNPKL svm = new DoubleQNPKL();
      svm.setC(Double.parseDouble(regularizationField.getText()));
      DebugPrinter.setDebugLevel(2);


      svm.train(localTrain);
      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;
      for (int s = 0; s < alphas.length; s++) {

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Examples of fr.lip6.jkernelmachines.classifier.DoublePegasosSVM.train()

      peg.setLambda(lambda);
      peg.setK(K);
      peg.setT(T);
      peg.setT0(t0);
      peg.setBias(bias);
      peg.train(train);




      //3.2 train transductive pegasos
//      DoubleGaussL2 kernel = new DoubleGaussL2();
//      kernel.setGamma(0.01);

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Examples of fr.lip6.jkernelmachines.classifier.DoubleQNPKL.train()

        .toString())) {
      DoubleQNPKL svm = new DoubleQNPKL();
      svm.setC(Double.parseDouble(regularizationField.getText()));
      DebugPrinter.setDebugLevel(2);


      svm.train(localTrain);
      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      double[] alphas = svm.getAlphas();
      int sv = 0;
      for (int s = 0; s < alphas.length; s++) {

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Examples of fr.lip6.jkernelmachines.classifier.DoubleSAG.train()

        .toString())) {
      DoubleSAG svm = new DoubleSAG();
      svm.setLambda(1. / (train.size() * Double
          .parseDouble(regularizationField.getText())));
      svm.setE(10);
      svm.train(localTrain);


      // info
      classnameLabel.setText(svm.getClass().getSimpleName());
      svLabel.setText("N/A");

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