Package org.drip.learning.bound

Class CoveringNumberBoundBuilder

java.lang.Object
org.drip.learning.bound.CoveringNumberBoundBuilder
public class CoveringNumberBoundBuilder
extends java.lang.Object
CoveringNumberBoundBuilder constructs the CoveringNumberProbabilityBound Instances for specific Learning Situations. The References are:

  • 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

  • Field Details

    • EPSILON_EXPONENT_REGRESSION_LEARNING

      public static final double EPSILON_EXPONENT_REGRESSION_LEARNING
      Epsilon Exponent for Regression Learning
      See Also:
      Constant Field Values

    • EPSILON_EXPONENT_AGNOSTIC_LEARNING

      public static final double EPSILON_EXPONENT_AGNOSTIC_LEARNING
      Epsilon Exponent for Agnostic Learning
      See Also:
      Constant Field Values

    • EPSILON_EXPONENT_AGNOSTIC_CONVEX_LEARNING

      public static final double EPSILON_EXPONENT_AGNOSTIC_CONVEX_LEARNING
      Epsilon Exponent for Agnostic Learning with Convex Functions
      See Also:
      Constant Field Values

  • Constructor Details

    • CoveringNumberBoundBuilder

      public CoveringNumberBoundBuilder()

  • Method Details

    • RegressionLearning

      public static final CoveringNumberLossBound RegressionLearning​(R1ToR1 funcSampleCoefficient, double dblExponentScaler)
      Construct the Regression Learning CoveringNumberProbabilityBound Instance
      Parameters:
      funcSampleCoefficient - The Sample Coefficient Function
      dblExponentScaler - The Exponent Scaler
      Returns:
      The Regression Learning CoveringNumberProbabilityBound Instance

    • AgnosticLearning

      public static final CoveringNumberLossBound AgnosticLearning​(R1ToR1 funcSampleCoefficient, double dblExponentScaler)
      Construct the Agnostic Learning CoveringNumberProbabilityBound Instance
      Parameters:
      funcSampleCoefficient - The Sample Coefficient Function
      dblExponentScaler - The Exponent Scaler
      Returns:
      The Agnostic Learning CoveringNumberProbabilityBound Instance

    • AgnosticConvexLearning

      public static final CoveringNumberLossBound AgnosticConvexLearning​(R1ToR1 funcSampleCoefficient, double dblExponentScaler)
      Construct the Agnostic Convex Learning CoveringNumberProbabilityBound Instance
      Parameters:
      funcSampleCoefficient - The Sample Coefficient Function
      dblExponentScaler - The Exponent Scaler
      Returns:
      The Agnostic Convex Learning CoveringNumberProbabilityBound Instance