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Transcript
Ensemble Methods

“No free lunch theorem”
Wolpert and Macready 1995
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
“No free lunch theorem”
Wolpert and Macready 1995
Solution search also involves searching for
learners

Different algorithms


Different algorithms
Different parameters
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Different algorithms
Different parameters
Different input representations/features
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Different algorithms
Different parameters
Different input representations/features
Different data
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Base learner

Diversity over accuracy
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Model combination

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Voting
Bagging
Boosting
Cascading

Data set = [1,2,3,4,5,6,7,8,9,10]

Samples:
 Input to learner 1 = [10,2,5,10,3]
 Input to learner 2 = [4,5,2,7,6,3]
 Input to learner 3 = [8,8,4,9,1]
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Create complementary learners
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
Create complementary learners
Train successive learners on the mistakes of
predecessors

Weak learners combine to a strong learner
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Adaboost – Adaptive Boosting
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
Adaboost – Adaptive Boosting
Allows for a smaller training set
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Adaboost – Adaptive Boosting
Allows for a smaller training set
Simple classifiers

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
Adaboost – Adaptive Boosting
Allows for a smaller training set
Simple classifiers
Binary

Modify probability of drawing examples from
a training set based on errors
Step 3


1
1 error
1  log(
)
2
error
error  0.33
1
1 .33
1  log(
)
2
.33
1  0.35

Demo

Sequence classifiers by complexity
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
Sequence classifiers by complexity
Use classifier j+1 if classifier j doesn’t meet a
confidence threshold



Sequence classifiers by complexity
Use classifier j+1 if classifier j doesn’t meet a
confidence threshold
Train cascading classifiers on instances the
previous classifier is not confident about




Sequence classifiers by complexity
Use classifier j+1 if classifier j doesn’t meet a
confidence threshold
Train cascading classifiers on instances the
previous classifier is not confident about
Most examples classified quickly, harder ones
passed to more expensive classifiers

Boosting and Cascading
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Object detection/tracking
Collaborative filtering
Neural networks
Optical character recognition ++
Biometrics
Data mining

Ensemble methods are proven effective,
but why?