Examples of edu.stanford.nlp.classify.LinearClassifierFactory

edu.stanford.nlp.classify.LinearClassifierFactory

Builds various types of linear classifiers, with functionality for setting objective function, optimization method, and other parameters. Classifiers can be defined with passed constructor arguments or using setter methods. Defaults to Quasi-newton optimization of a {@code LogConditionalObjectiveFunction}. (Merges old classes: CGLinearClassifierFactory, QNLinearClassifierFactory, and MaxEntClassifierFactory.) Note that a bias term is not assumed, and so if you want to learn a bias term you should add an "always-on" feature to your examples. @author Jenny Finkel @author Chris Cox (merged factories, 8/11/04) @author Dan Klein (CGLinearClassifierFactory, MaxEntClassifierFactory) @author Galen Andrew (tuneSigma), @author Marie-Catherine de Marneffe (CV in tuneSigma) @author Sarah Spikes (Templatization, though I don't know what to do with the Minimizer) @author Ramesh Nallapati (nmramesh@cs.stanford.edu) {@link #trainSemiSupGE} methods


    verbose("Making classifier...");
    QNMinimizer minim = new QNMinimizer();//new ResultStoringMonitor(5, "weights"));
//    minim.shutUp();


    LinearClassifierFactory factory = new LinearClassifierFactory(minim);
    factory.setTol(tol);
    factory.setSigma(sigma);
    if (tuneSigma) {
      factory.setTuneSigmaHeldOut();
    }
    scorer = factory.trainClassifier(data);


    verbose("Done training.");
  }

Examples of edu.stanford.nlp.classify.LinearClassifierFactory

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