Package org.drip.learning.regularization
Class RegularizerRdContinuousToR1Continuous
java.lang.Object
org.drip.spaces.rxtor1.NormedRxToNormedR1
org.drip.spaces.rxtor1.NormedRdToNormedR1
org.drip.spaces.rxtor1.NormedRdContinuousToR1Continuous
org.drip.learning.regularization.RegularizerRdContinuousToR1Continuous
- All Implemented Interfaces:
RegularizerRdToR1
public class RegularizerRdContinuousToR1Continuous extends NormedRdContinuousToR1Continuous implements RegularizerRdToR1
RegularizerRdContinuousToR1Continuous computes the Structural Loss and Risk for the specified
Normed Rd Continuous To Normed R1 Continuous Learning Function.
- Alon, N., S. Ben-David, N. Cesa Bianchi, and D. Haussler (1997): Scale-sensitive Dimensions, Uniform Convergence, and Learnability Journal of Association of Computational Machinery 44 (4) 615-631
- Anthony, M., and P. L. Bartlett (1999): Artificial Neural Network Learning - Theoretical Foundations Cambridge University Press Cambridge, UK
- Kearns, M. J., R. E. Schapire, and L. M. Sellie (1994): Towards Efficient Agnostic Learning Machine Learning 17 (2) 115-141
- Lee, W. S., P. L. Bartlett, and R. C. Williamson (1998): The Importance of Convexity in Learning with Squared Loss IEEE Transactions on Information Theory 44 1974-1980
- Vapnik, V. N. (1998): Statistical learning Theory Wiley New York
- Module = Computational Core Module
- Library = Statistical Learning
- Project = Agnostic Learning Bounds under Empirical Loss Minimization Schemes
- Package = Statistical Learning Empirical Loss Regularizer
- Author:
- Lakshmi Krishnamurthy
-
Constructor Summary
Constructors Constructor Description RegularizerRdContinuousToR1Continuous(RdToR1 funcRdToR1, RdContinuousBanach rdContinuousInput, R1Continuous r1ContinuousOutput, double dblLambda)RegularizerRdContinuousToR1Continuous Function Space Constructor -
Method Summary
Modifier and Type Method Description doublelambda()Retrieve the Regularization Constant LambdadoublestructuralLoss(RdToR1 funcRdToR1, double[][] aadblX)Compute the Regularization Sample Structural LossdoublestructuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY)Compute the Regularization Sample Structural LossMethods inherited from class org.drip.spaces.rxtor1.NormedRdContinuousToR1Continuous
populationMetricNormMethods inherited from class org.drip.spaces.rxtor1.NormedRdToNormedR1
function, inputMetricVectorSpace, outputMetricVectorSpace, populationESS, sampleMetricNorm, sampleSupremumNormMethods inherited from class org.drip.spaces.rxtor1.NormedRxToNormedR1
populationCoveringNumber, populationSupremumCoveringNumber, populationSupremumMetricNorm, sampleCoveringNumber, sampleSupremumCoveringNumberMethods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
-
Constructor Details
-
RegularizerRdContinuousToR1Continuous
public RegularizerRdContinuousToR1Continuous(RdToR1 funcRdToR1, RdContinuousBanach rdContinuousInput, R1Continuous r1ContinuousOutput, double dblLambda) throws java.lang.ExceptionRegularizerRdContinuousToR1Continuous Function Space Constructor- Parameters:
funcRdToR1- The R^d To R^1 FunctionrdContinuousInput- The Continuous R^d Input Metric Vector Spacer1ContinuousOutput- The Continuous R^1 Output Metric Vector SpacedblLambda- The Regularization Lambda- Throws:
java.lang.Exception- Thrown if the Inputs are Invalid
-
-
Method Details
-
lambda
public double lambda()Description copied from interface:RegularizerRdToR1Retrieve the Regularization Constant Lambda- Specified by:
lambdain interfaceRegularizerRdToR1- Returns:
- The Regularization Constant Lambda
-
structuralLoss
Description copied from interface:RegularizerRdToR1Compute the Regularization Sample Structural Loss- Specified by:
structuralLossin interfaceRegularizerRdToR1- Parameters:
funcRdToR1- R^d To R^1 Function InstanceaadblX- The Sample Instance- Returns:
- The Regularization Sample Structural Loss
- Throws:
java.lang.Exception- Thrown if the Inputs are Invalid
-
structuralRisk
public double structuralRisk(RdR1 distRdR1, RdToR1 funcRdToR1, double[][] aadblX, double[] adblY) throws java.lang.ExceptionDescription copied from interface:RegularizerRdToR1Compute the Regularization Sample Structural Loss- Specified by:
structuralRiskin interfaceRegularizerRdToR1- Parameters:
distRdR1- R^d R^1 Multivariate MeasurefuncRdToR1- R^d To R^1 Function InstanceaadblX- The Sample InstanceadblY- The Response Instance- Returns:
- The Regularization Sample Structural Loss
- Throws:
java.lang.Exception- Thrown if the Inputs are Invalid
-