Package org.drip.learning.regularization

Class RegularizerRdCombinatorialToR1Continuous

java.lang.Object
org.drip.spaces.rxtor1.NormedRxToNormedR1
org.drip.spaces.rxtor1.NormedRdToNormedR1
org.drip.spaces.rxtor1.NormedRdCombinatorialToR1Continuous
org.drip.learning.regularization.RegularizerRdCombinatorialToR1Continuous
All Implemented Interfaces:
RegularizerRdToR1
public class RegularizerRdCombinatorialToR1Continuous
extends NormedRdCombinatorialToR1Continuous
implements RegularizerRdToR1
RegularizerRdCombinatorialToR1Continuous computes the Structural Loss and Risk for the specified Normed Rd Combinatorial 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


Author:
Lakshmi Krishnamurthy

  • Constructor Details

    • RegularizerRdCombinatorialToR1Continuous

      public RegularizerRdCombinatorialToR1Continuous​(RdToR1 funcRegularizerRdToR1, RdCombinatorialBanach rdCombinatorialInput, R1Continuous r1ContinuousOutput, double dblLambda) throws java.lang.Exception
      RegularizerRdCombinatorialToR1Continuous Function Space Constructor
      Parameters:
      funcRegularizerRdToR1 - The R^d To R^1 Regularizer Function
      rdCombinatorialInput - The Combinatorial R^d Input Metric Vector Space
      r1ContinuousOutput - The Continuous R^1 Output Metric Vector Space
      dblLambda - The Regularization Lambda
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid

  • Method Details

    • lambda

      public double lambda()
      Description copied from interface: RegularizerRdToR1
      Retrieve the Regularization Constant Lambda
      Specified by:
      lambda in interface RegularizerRdToR1
      Returns:
      The Regularization Constant Lambda

    • structuralLoss

      public double structuralLoss​(RdToR1 funcRdToR1, double[][] aadblX) throws java.lang.Exception
      Description copied from interface: RegularizerRdToR1
      Compute the Regularization Sample Structural Loss
      Specified by:
      structuralLoss in interface RegularizerRdToR1
      Parameters:
      funcRdToR1 - R^d To R^1 Function Instance
      aadblX - 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.Exception
      Description copied from interface: RegularizerRdToR1
      Compute the Regularization Sample Structural Loss
      Specified by:
      structuralRisk in interface RegularizerRdToR1
      Parameters:
      distRdR1 - R^d R^1 Multivariate Measure
      funcRdToR1 - R^d To R^1 Function Instance
      aadblX - The Sample Instance
      adblY - The Response Instance
      Returns:
      The Regularization Sample Structural Loss
      Throws:
      java.lang.Exception - Thrown if the Inputs are Invalid