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On PAC-Bayesian bounds for random forests
Stephan S. Lorenzen
,
Christian Igel
*
,
Yevgeny Seldin
*
Corresponding author for this work
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Dive into the research topics of 'On PAC-Bayesian bounds for random forests'. Together they form a unique fingerprint.
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Keyphrases
Random Forest
100%
Majority Vote
100%
PAC-Bayesian Bounds
100%
PAC-Bayesian
66%
Generalization Bounds
33%
Holdout
33%
Generalization Error
33%
Individual Choice
33%
Ensemble Member
33%
Bayesian Approach
33%
Machine Learning Techniques
33%
Validation Set
33%
Benchmark Dataset
33%
Random Forest Algorithm
33%
Decision Tree Ensemble
33%
Decision Tree
33%
Performance Guarantee
33%
Tight
33%
Limited Training Data
33%
Training Process
33%
Computer Science
Gibbs Classifier
50%
Individual Classifier
50%
Ensemble Member
25%
Individual Decision
25%