MaltParser 1.4.1

Uses of Interface
org.maltparser.parser.guide.instance.InstanceModel

Packages that use InstanceModel
org.maltparser.ml.liblinear Contains classes that interacts with the Liblinear learner. 
org.maltparser.ml.libsvm Contains classes that interacts with the LIBSVM learner. 
org.maltparser.parser.guide.instance Provides classes for different instance models. 
 

Uses of InstanceModel in org.maltparser.ml.liblinear
 

Fields in org.maltparser.ml.liblinear declared as InstanceModel
protected  InstanceModel Liblinear.owner
           
 

Methods in org.maltparser.ml.liblinear that return InstanceModel
 InstanceModel Liblinear.getOwner()
           
 

Methods in org.maltparser.ml.liblinear with parameters of type InstanceModel
protected  void Liblinear.setOwner(InstanceModel owner)
           
 

Constructors in org.maltparser.ml.liblinear with parameters of type InstanceModel
Liblinear(InstanceModel owner, Integer learnerMode)
          Constructs a Liblinear learner.
 

Uses of InstanceModel in org.maltparser.ml.libsvm
 

Fields in org.maltparser.ml.libsvm declared as InstanceModel
protected  InstanceModel Libsvm.owner
           
 

Methods in org.maltparser.ml.libsvm that return InstanceModel
 InstanceModel Libsvm.getOwner()
           
 

Methods in org.maltparser.ml.libsvm with parameters of type InstanceModel
protected  void Libsvm.setOwner(InstanceModel owner)
           
 

Constructors in org.maltparser.ml.libsvm with parameters of type InstanceModel
Libsvm(InstanceModel owner, Integer learnerMode)
          Constructs a LIBSVM learner.
 

Uses of InstanceModel in org.maltparser.parser.guide.instance
 

Classes in org.maltparser.parser.guide.instance that implement InstanceModel
 class AtomicModel
           
 class DecisionTreeModel
          This class implements a decision tree model.
 class FeatureDivideModel
          The feature divide model is used for divide the training instances into several models according to a divide feature.
 


MaltParser 1.4.1

Copyright 2007-2010 Johan Hall, Jens Nilsson and Joakim Nivre.