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java.lang.Object hu.birot.OTKit.learning.OnlineLearningExamples
public class OnlineLearningExamples
Provides concrete online learning algorithms.
OnlineLearning
Constructor Summary  

OnlineLearningExamples()

Method Summary  

static OnlineLearning 
allDemotion(double plasticity)
Errordriven online learning, demoting all loser preferring constraints by plasticity . 
static OnlineLearning 
demotionOnly(double plasticity)
Errordriven onine learning, demoting the highest ranked loser prefering constraint by w*plasticity , where w is the number of winner preferring constraints. 
static OnlineLearning 
EDCD()
Errordriven online learning, using Tesar and Smolensky's update rule. 
static OnlineLearning 
ErrorDriven(ConstraintMotion cm)
General errordriven update of the grammar, based on a piece of learning data. 
static OnlineLearning 
GLA(double plasticity)
Errordriven online learning, using Boersma's update rule. 
static OnlineLearning 
localBoersma(double plasticity)

static OnlineLearning 
localMagri(double plasticity)

static OnlineLearning 
localOptima(ConstraintMotion cm_w,
ConstraintMotion cm_l)
An approach to online learning based on the heuristic that the winner must be locally optimal, and the loser must not. 
static OnlineLearning 
localOptimaReversed(ConstraintMotion cm_w,
ConstraintMotion cm_l)
An approach to online learning based on the heuristic that both the winner and the loser must be locally optimal. 
static OnlineLearning 
Magri(double plasticity)
Errordriven online learning, using Magri's update rule. 
static OnlineLearning 
singleDemotionOnly(double plasticity)
Errordriven online learning, demoting only the highest ranked loser preferring constraint by plasticity . 
Methods inherited from class java.lang.Object 

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

public OnlineLearningExamples()
Method Detail 

public static OnlineLearning ErrorDriven(ConstraintMotion cm)
General errordriven update of the grammar, based on a piece of learning data.
The learn(Grammar G, Production P, Candidate cand) method of this OnlineLearning works as follows:
The learner is given the piece of data cand (the "winner"). She extracts the underlying form cand.uf, and employs her Grammar G and method of production P to produce her output for cand.uf (the "loser"). Finally, she updates her grammar G based on the discrepancy between the winner and the loser, by employing the update rule defined by parameter cm.
cm
 Method of updating the grammar, given a winnerloser pair.
public static OnlineLearning GLA(double plasticity)
Errordriven online learning, using Boersma's update rule.
plasticity
 The plasticity in Boersma's update rule.
ConstraintMotionExamples.Boersma(double)
public static OnlineLearning Magri(double plasticity)
Errordriven online learning, using Magri's update rule.
plasticity
 The plasticity in Magri's update rule.
ConstraintMotionExamples.Magri(double)
public static OnlineLearning demotionOnly(double plasticity)
Errordriven onine learning, demoting the highest ranked loser prefering constraint
by w*plasticity
, where w
is the number of winner preferring constraints.
plasticity

ConstraintMotionExamples.demotionOnly(double)
public static OnlineLearning singleDemotionOnly(double plasticity)
Errordriven online learning, demoting only the highest ranked loser preferring constraint by
plasticity
.
plasticity

ConstraintMotionExamples.singleDemotionOnly(double)
public static OnlineLearning allDemotion(double plasticity)
Errordriven online learning, demoting all loser preferring constraints by
plasticity
.
plasticity

ConstraintMotionExamples.allDemotion(double)
public static OnlineLearning EDCD()
Errordriven online learning, using Tesar and Smolensky's update rule. See ConstraintMotionExamples.TesarSmolensky(plasticity).
public static OnlineLearning localOptima(ConstraintMotion cm_w, ConstraintMotion cm_l)
An approach to online learning based on the heuristic that the winner must be locally optimal, and the loser must not.
This method returns an instance of OnlineLearning whose learn(Grammar G, Production P, Candidate cand) method works as follows: Candidate cand is the winner (piece of learning data given to the learner), whereas a loser is produced by P based on G. Let bnw be the best neighbor of the winner (cand) and let bnl be the best neighbor of the loser, both with respect to grammar G (the topology and hierarchy in G). Subsequently, G is updated by cm_w with cand taking the winner's position and bnw taking the loser's position (the idea being that cand must be better than its best neighbor). Finally, G is further updated by cm_l with bnl taking the winner's position and the loser taking the loser's position (the idea being that the loser must be worse than its best neighbor).
cm_w
 Update method used to implement the idea that winner must be better than its best neighbor.cm_l
 Update method used to implement the idea that loser must be worse than its best neighbor.
public static OnlineLearning localBoersma(double plasticity)
public static OnlineLearning localMagri(double plasticity)
public static OnlineLearning localOptimaReversed(ConstraintMotion cm_w, ConstraintMotion cm_l)
An approach to online learning based on the heuristic that both the winner and the loser must be locally optimal.
This method returns an instance of OnlineLearning whose learn(Grammar G, Production P, Candidate cand) method works as follows: Candidate cand is the winner (piece of learning data given to the learner), whereas a loser is produced by P based on G. Let bnw be the best neighbor of the winner (cand) and let bnl be the best neighbor of the loser, both with respect to grammar G (the topology and hierarchy in G). Subsequently, G is updated by cm_w with cand taking the winner's position and bnw taking the loser's position (the idea being that cand must be better than its best neighbor). Finally, G is further updated by cm_l with the loser taking the winner's position and bnl taking the loser's position (the idea being that the loser must be better than its best neighbor).
In fact, the heuristic is that the loser should not be locally optimal. Therefore cm_l is usually a constraint demotion with a negative plasticity value.
cm_w
 Update method used to implement the idea that winner must be better than its best neighbor.cm_l
 Update method used to implement the idea that loser must be worse than its best neighbor.


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