Examples of NaiveBayes

classification.NaiveBayes
uk.ac.cam.ha293.tweetlabel.classify.NaiveBayes
weka.classifiers.bayes.NaiveBayes
Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

For more information on Naive Bayes classifiers, see

George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
BibTeX:
```
 @inproceedings{John1995, address = {San Mateo}, author = {George H. John and Pat Langley}, booktitle = {Eleventh Conference on Uncertainty in Artificial Intelligence}, pages = {338-345}, publisher = {Morgan Kaufmann}, title = {Estimating Continuous Distributions in Bayesian Classifiers}, year = {1995} } 
```
Valid options are:
```
 -K Use kernel density estimator rather than normal distribution for numeric attributes
```
```
 -D Use supervised discretization to process numeric attributes 
```
```
 -O Display model in old format (good when there are many classes) 
```
@author Len Trigg (trigg@cs.waikato.ac.nz) @author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5516 $

Examples of classification.NaiveBayes

    return h;
  }


  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    NaiveBayes nb = new NaiveBayes(0.1, 0.1, xA, yA);
    LinearClassifier h = nb.batchTrain(train);
    return h;
  }

View Full Code Here

Examples of classification.NaiveBayes

    return h;
  }


  public static LinearClassifier trainNaivBayes(
      ArrayList<ClassificationInstance> train, Alphabet xA, Alphabet yA) {
    NaiveBayes nb = new NaiveBayes(0.1, 0.1, xA, yA);
    LinearClassifier h = nb.batchTrain(train);
    return h;
  }

View Full Code Here

Examples of uk.ac.cam.ha293.tweetlabel.classify.NaiveBayes

  }
  
  //Note - erases existing naive bayes
  public void trainNaiveBayes() {
    System.out.println("Training Naive Bayes classifier");
    naiveBayes = new NaiveBayes(this);
    for(Category category : categories) {
      if(categoryBlacklistSet.contains(category.getTitle())) continue; //Don't train on blacklisted categories - should remove need for stopwords!
      for(String word : category.getWords()) {
        naiveBayes.trainLIWC(word, category);
      }

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

    }
    
    m_percentUsedByDT = (double)count / (m_theInstances.numAttributes() - 1);
    m_percentDeleted = numDeleted / (m_theInstances.numAttributes() -1);


    m_NB = new NaiveBayes();
    m_NB.buildClassifier(m_theInstances);


    m_dtInstances = new Instances(m_dtInstances, 0);
    m_theInstances = new Instances(m_theInstances, 0);
  }

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

  }
     }
     
     if (m_NB == null) {
  // construct naive bayes for the first time
  m_NB = new NaiveBayes();
  m_NB.buildClassifier(m_theInstances);
     }
     return fc;
   }

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

    }
    
    m_percentUsedByDT = (double)count / (m_theInstances.numAttributes() - 1);
    m_percentDeleted = numDeleted / (m_theInstances.numAttributes() -1);


    m_NB = new NaiveBayes();
    m_NB.buildClassifier(m_theInstances);


    m_dtInstances = new Instances(m_dtInstances, 0);
    m_theInstances = new Instances(m_theInstances, 0);
  }

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

  {
    try {
      switch (classifier)
      {
        case NAIVE_BAYES:
          return new NaiveBayes();
        case J48:
          J48 j48 = new J48();      
          j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
          return j48;
//        case SMO:

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

  }
     }
     
     if (m_NB == null) {
  // construct naive bayes for the first time
  m_NB = new NaiveBayes();
  m_NB.buildClassifier(m_theInstances);
     }
     return fc;
   }

View Full Code Here

Examples of weka.classifiers.bayes.NaiveBayes

  {
    try {
      switch (classifier)
      {
        case NAIVE_BAYES:
          return new NaiveBayes();
        case J48:
          J48 j48 = new J48();      
          j48.setOptions(new String[] { "-C", "0.25", "-M", "2" });
          return j48;
        case SMO:

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.