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Entropy Properties of a Decision Rule Class in Connection with machine learning abilities
Published on Oct 08, 20076182 Views
Many methods of Machine Learning are based on the idea of empirical risk minimisation. It is to find a decision rule or a model from some set which most perfectly fits the data presented in the traini
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Chapter list
Entropy Properties of a Decision Rule Class in Connection with machine learning abilities00:00
Learning to Pattern Recognition03:05
Reconstruction of Numerical Dependencies04:31
Over Fitting in Pattern Recognition Problem06:16
Reconstruction of Numerical Dependencies (a)06:22
Over Fitting in Pattern Recognition Problem (a)06:51
Over Fitting in Polynomial Regression Reconstruction07:57
Formal Definition10:42
Dependence of the Empirical and True Risk12:40
Uniform Convergence of Frequencies to Probabilities14:23
Random Functions16:52
Conditions of the Uniform Convergence of Frequencies to Probabilities pt 118:32
Conditions of the Uniform Convergence of Frequencies to Probabilities pt 220:08
Conditions of the Uniform Convergence of Frequencies to Probabilities pt 321:55
Conditions for the Uniform Convergence of Means to Expectations25:05
We Get Only Sufficient Conditions pt 126:51
We Get Only Sufficient Conditions pt 227:03
We Get Only Sufficient Conditions pt 1 (a)28:52
We Get Only Sufficient Conditions pt 329:43
We Get Only Sufficient Conditions pt 431:01
Average Distance between Them Is Non-zero32:10
We Get Only Sufficient Conditions pt 4 (a)32:45
When the Uniform Convergence of Frequencies to Probabilities Does not Hold33:48
We Get Only Sufficient Conditions pt 2 (a)37:08
When the Uniform Convergence of Frequencies to Probabilities Does not Hold (a)38:26