it.uniroma2.sag.kelp.learningalgorithm.classification.hmm

Class SequenceClassificationLinearLearningAlgorithm

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm

it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLinearLearningAlgorithm

All Implemented Interfaces:

LearningAlgorithm, LinearMethod, MetaLearningAlgorithm

public class SequenceClassificationLinearLearningAlgorithm
extends SequenceClassificationLearningAlgorithm
implements LinearMethod

This class implements a sequential labeling paradigm.
Given sequences of items (each implemented as an Example and associated to one Label) this class allow to apply a generic LearningAlgorithm to use the "history" of each item in the sequence in order to improve the classification quality. In other words, the classification of each example does not depend only its representation, but it also depend on its "history", in terms of the classed assigned to the preceding examples.
This class should be used when a linear learning algorithm is used, thus directly operating in the representation space.
This algorithms was inspired by the work of:
Y. Altun, I. Tsochantaridis, and T. Hofmann. Hidden Markov support vector machines. In Proceedings of the Twentieth International Conference on Machine Learning, 2003.

Author:

Danilo Croce

Constructor Summary

Constructors

Constructor and Description
SequenceClassificationLinearLearningAlgorithm(BinaryLearningAlgorithm baseLearningAlgorithm, int transitionsOrder, float transitionWeight)

Method Summary

Methods

Modifier and Type	Method and Description
LearningAlgorithm	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
LearningAlgorithm	getBaseAlgorithm() Returns the base algorithm this meta algorithm is based on
String	getRepresentation() Returns the representation this learning algorithm exploits
void	setBaseAlgorithm(LearningAlgorithm baseAlgorithm)
void	setRepresentation(String representationName) Sets the representation this learning algorithm will exploit

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.classification.hmm.SequenceClassificationLearningAlgorithm

getBaseLearningAlgorithm, getBeamSize, getLabels, getMaxEmissionCandidates, getPredictionFunction, getSequenceExampleGenerator, getTransitionsOrder, learn, reset, setBaseLearningAlgorithm, setBeamSize, setLabels, setMaxEmissionCandidates, setPredictionFunction, setSequenceExampleGenerator

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SequenceClassificationLinearLearningAlgorithm

public SequenceClassificationLinearLearningAlgorithm(BinaryLearningAlgorithm baseLearningAlgorithm,
                                             int transitionsOrder,
                                             float transitionWeight)
                                              throws Exception

Parameters:

baseLearningAlgorithm - the "linear" learning algorithm devoted to the acquisition of a model after that each example has been enriched with its "history"

transitionsOrder - given a targeted item in the sequence, this variable determines the number of previous example considered in the learning/labeling process.

transitionWeight - the importance of the transition-based features during the learning process. Higher valuers will assign more importance to the transitions.

Throws:

Exception - The input baseLearningAlgorithm is not a Linear method

Method Detail

duplicate

public LearningAlgorithm duplicate()

Description copied from interface: LearningAlgorithm

Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied

Specified by:

duplicate in interface LearningAlgorithm

getBaseAlgorithm

public LearningAlgorithm getBaseAlgorithm()

Description copied from interface: MetaLearningAlgorithm

Returns the base algorithm this meta algorithm is based on

Specified by:

getBaseAlgorithm in interface MetaLearningAlgorithm

Returns:

the base algorithm

getRepresentation

public String getRepresentation()

Description copied from interface: LinearMethod

Returns the representation this learning algorithm exploits

Specified by:

getRepresentation in interface LinearMethod

Returns:

the representation

setBaseAlgorithm

public void setBaseAlgorithm(LearningAlgorithm baseAlgorithm)

Specified by:

setBaseAlgorithm in interface MetaLearningAlgorithm

Parameters:

baseAlgorithm - the baseAlgorithm to set

setRepresentation

public void setRepresentation(String representationName)

Description copied from interface: LinearMethod

Sets the representation this learning algorithm will exploit

Specified by:

setRepresentation in interface LinearMethod

Parameters:

representationName - the representation to set