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Generalization bounds
Published on Feb 4, 20258659 Views
When a learning algorithm produces a classifier, a natural question to ask is "How well will it do in the future?" To make statements about the future given the past, some assumption must be made. If
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Presentation
Practical generalization Bounds00:01
Learning01:03
Why study ..03:45
Better Methods ...04:33
To gain ...05:41
Outline06:33
Model: Definitions07:22
Model:Derived quantities08:37
Model:Basic Observations10:33
Possible Error distributions11:15
Model:basic quantities12:30
test Set Bound15:26
Observation and20:18
True Error Bound21:28
Test Set...21:39
What does Test...22:19
True error31:40
Interpretation34:50
K-fold36:14
outline41:21
Trainig Set...41:54
Occam`s Razor...45:11
Occam Bound...51:45
True Error...52:01
test Set...55:33