01_25_2020.txt

Features:

Bug Fixes/Re-organization:

	- Learning Kernel Integral Operator Eigen Container (1, 2, 3)
	- Learning Kernel Mercer Kernel (4, 5, 6)
	- Learning Kernel Symmetric R^d To Normal R^1 Kernel (7, 8, 9)
	- Learning Kernel Symmetric R^d To Normal R^d Kernel (10, 11, 12)
	- Learning Regularization Regularization Function (13, 14, 15)
	- Learning Regularization Regularizer Builder (16, 17, 18)
	- Learning Regularization Regularizer R^1 Combinatorial To R^1 Continuous (19, 20, 21)
	- Learning Regularization Regularizer R^1 Continuous To R^1 Continuous (22, 23, 24)
	- Learning Regularization Regularizer R^1 To R^1 (25, 26, 27)
	- Learning Regularization Regularizer R^d Combinatorial To R^1 Continuous (28, 29, 30)
	- Learning Regularization Regularizer R^d Continuous To R^1 Continuous (31, 32, 33)
	- Learning Regularization Regularizer R^d To R^1 (34, 35, 36)
	- Learning R^x To R^1 Approximate Lipschitz Loss Learner (37, 38, 39)
	- Learning R^x To R^1 Empirical Learning Metric Estimator (40, 41, 42)
	- Learning R^x To R^1 Empirical Penalty Supremum (43, 44, 45)
	- Learning R^x To R^1 Empirical Penalty Supremum Estimator (46, 47, 48)
	- Learning R^x To R^1 Empirical Penalty Supremum Metrics (49, 50, 51)
	- Learning R^x To R^1 Generalized Learner (52, 53, 54)
	- Learning R^x To R^1 L^1 Loss Learner (55, 56, 57)
	- Learning R^x To R^1 Lipschitz Loss Learner (58, 59, 60)


Samples: