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Entropy Properties of a Decision Rule Class in Connection with machine learning abilities
Published on 2007-10-086190 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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Presentation
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