0.25
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1.75
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Generalization bounds
Published on Feb 25, 20078641 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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Chapter list
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:Derived quantities09:14
Model:Basic Observations10:33
Possible Error distributions11:15
Model:basic quantities12:30
Model:basic quantities13:30
Outline15:14
test Set Bound15:26
test Set Bound15:52
Observation and20:18
Observation and21:03
True Error Bound21:28
Test Set...21:39
What does Test...22:19
Test Set...26:09
Test Set...31:06
True error31:40
Test Set...34:40
Interpretation34:50
K-fold36:14
outline41:21
Trainig Set...41:54
Occam`s Razor...45:11
Occam`s Razor...46:21
Occam`s Razor...48:28
Occam`s Razor...49:01
Occam`s Razor...49:21
Occam`s Razor...50:46
Occam`s Razor...51:02
Occam Bound...51:45
True Error...52:01
Occam`s Razor...53:35
Occam`s Razor...54:17
test Set...55:33