it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm

Class RandomizedBudgetPerceptron

java.lang.Object

it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm

it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.RandomizedBudgetPerceptron

All Implemented Interfaces:

BinaryLearningAlgorithm, KernelMethod, LearningAlgorithm, MetaLearningAlgorithm, OnlineLearningAlgorithm

public class RandomizedBudgetPerceptron
extends BudgetedLearningAlgorithm
implements MetaLearningAlgorithm

It is a variation of the Randomized Budget Perceptron proposed in

[CavallantiCOLT2006] G. Cavallanti, N. Cesa-Bianchi, C. Gentile. Tracking the best hyperplane with a simple budget Perceptron. In proc. of the 19-th annual conference on Computational Learning Theory. (2006)

Until the budget is not reached the online learning updating policy is the one of the baseAlgorithm that this meta-algorithm is exploiting. When the budget is full, a random support vector is deleted and the perceptron updating policy is adopted

Author:

Simone Filice

Field Summary

Fields inherited from class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm

budget, label

Constructor Summary

Constructors

Constructor and Description
RandomizedBudgetPerceptron()
RandomizedBudgetPerceptron(int budget, OnlineLearningAlgorithm baseAlgorithm, long seed, Label label)

Method Summary

Methods

Modifier and Type	Method and Description
RandomizedBudgetPerceptron	duplicate() Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
OnlineLearningAlgorithm	getBaseAlgorithm() Returns the base algorithm this meta algorithm is based on
Kernel	getKernel() Returns the kernel exploited by this learner
PredictionFunction	getPredictionFunction() Returns the predictionFunction learned during the training process
protected Prediction	predictAndLearnWithAvailableBudget(Example example)
protected Prediction	predictAndLearnWithFullBudget(Example example) Learns from a single example applying a specific policy that must be adopted when the budget is reached
void	reset() Resets all the learning process, returning to the default state.
void	setBaseAlgorithm(LearningAlgorithm baseAlgorithm)
void	setKernel(Kernel kernel) Sets the kernel this
void	setSeed(long seed) Sets the seed for the random generator adopted to select the support vector to delete

Methods inherited from class it.uniroma2.sag.kelp.learningalgorithm.budgetedAlgorithm.BudgetedLearningAlgorithm

getBudget, getLabel, getLabels, learn, learn, setBudget, setLabel, setLabels

Methods inherited from class java.lang.Object

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

Methods inherited from interface it.uniroma2.sag.kelp.learningalgorithm.LearningAlgorithm

getLabels, learn, setLabels

Constructor Detail

RandomizedBudgetPerceptron

public RandomizedBudgetPerceptron()

RandomizedBudgetPerceptron

public RandomizedBudgetPerceptron(int budget,
                          OnlineLearningAlgorithm baseAlgorithm,
                          long seed,
                          Label label)

Method Detail

setSeed

public void setSeed(long seed)

Sets the seed for the random generator adopted to select the support vector to delete

Parameters:

seed - the seed of the randomGenerator

duplicate

public RandomizedBudgetPerceptron 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

reset

public void reset()

Description copied from interface: LearningAlgorithm

Resets all the learning process, returning to the default state. All the learned process is forgotten

Specified by:

reset in interface LearningAlgorithm

predictAndLearnWithFullBudget

protected Prediction predictAndLearnWithFullBudget(Example example)

Description copied from class: BudgetedLearningAlgorithm

Learns from a single example applying a specific policy that must be adopted when the budget is reached

Specified by:

predictAndLearnWithFullBudget in class BudgetedLearningAlgorithm

Parameters:

example - the example to be exploited in the learning process

Returns:

the prediction on the given example, using the old classification function (before any possible update)

setBaseAlgorithm

public void setBaseAlgorithm(LearningAlgorithm baseAlgorithm)

Specified by:

setBaseAlgorithm in interface MetaLearningAlgorithm

Parameters:

baseAlgorithm - the baseAlgorithm to set

getBaseAlgorithm

public OnlineLearningAlgorithm 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

getPredictionFunction

public PredictionFunction getPredictionFunction()

Description copied from interface: LearningAlgorithm

Returns the predictionFunction learned during the training process

Specified by:

getPredictionFunction in interface LearningAlgorithm

Returns:

the predictionFunction learned during the training process

getKernel

public Kernel getKernel()

Description copied from interface: KernelMethod

Returns the kernel exploited by this learner

Specified by:

getKernel in interface KernelMethod

Returns:

the kernel

setKernel

public void setKernel(Kernel kernel)

Description copied from interface: KernelMethod

Sets the kernel this

Specified by:

setKernel in interface KernelMethod

Parameters:

kernel - the kernel to set

predictAndLearnWithAvailableBudget

protected Prediction predictAndLearnWithAvailableBudget(Example example)

Specified by:

predictAndLearnWithAvailableBudget in class BudgetedLearningAlgorithm