| Interface | Description |
|---|---|
| EmpiricalLearningMetricEstimator |
EmpiricalLearningMetricEstimator is the Estimator of the Empirical Loss and Risk, as well as the
corresponding Covering Numbers.
|
| Class | Description |
|---|---|
| ApproximateLipschitzLossLearner |
ApproximateLipschitzLossLearner implements the Learner Class that holds the Space of Normed R^d To Normed
R^1 Learning Functions for the Family of Loss Functions that are "approximately" Lipschitz, i.e.,
loss (ep) - loss (ep') Less Than max (C * |ep-ep'|, C')
The References are:
1) Alon, N., S.
|
| EmpiricalPenaltySupremum |
EmpiricalPenaltySupremum holds the Learning Function that corresponds to the Empirical Supremum, as well
as the corresponding Supremum Value.
|
| EmpiricalPenaltySupremumEstimator |
EmpiricalPenaltySupremumEstimator contains the Implementation of the Empirical Penalty Supremum Estimator
dependent on Multivariate Random Variables where the Multivariate Function is a Linear Combination of
Bounded Univariate Functions acting on each Random Variate.
|
| EmpiricalPenaltySupremumMetrics |
EmpiricalPenaltySupremumMetrics computes Efron-Stein Metrics for the Penalty Supremum R^x To R^1
Functions.
|
| GeneralizedLearner |
GeneralizedLearner implements the Learner Class that holds the Space of Normed R^x To Normed R^1 Learning
Functions along with their Custom Empirical Loss.
|
| L1LossLearner |
L1LossLearner implements the Learner Class that holds the Space of Normed R^x To Normed R^1 Learning
Functions that employs L1 Empirical Loss Routine.
|
| LipschitzLossLearner |
LipschitzLossLearner implements the Learner Class that holds the Space of Normed R^1 To Normed R^1
Learning Functions for the Family of Loss Functions that are Lipschitz, i.e.,
loss (ep) - loss (ep') Less Than C * |ep-ep'|
The References are:
1) Alon, N., S.
|
| LpLossLearner |
LpLossLearner implements the Learner Class that holds the Space of Normed R^x To Normed R^1 Learning
Functions for the Family of Loss Functions that are Polynomial, i.e.,
loss (eta) = (eta ^ p) / p, for p greater than 1.
|