Searching for authors named "John Langford" – sorted by Relevance.
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Relating reinforcement learning performance to classification performance
- Relating Reinforcement Learning Performance to Classification Performance John Langford jl
- Cited by 7 (2 self) – Add To MetaCart
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2006) Predicting conditional quantiles via reduction to classification
- Predicting Conditional Quantiles via Reduction to Classification John Langford Toyota
- Cited by 2 (1 self) – Add To MetaCart
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Quantitatively Tight Sample Complexity Bounds
- Quantitatively Tight Sample Complexity Bounds John Langford I present many new results on sample
- Cited by 4 (1 self) – Add To MetaCart
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Combining Train Set and Test Set Bounds
- Combining Train Set and Test Set Bounds John Langford jcl@(email omitted); Computer Science Department
- Cited by 4 (2 self) – Add To MetaCart
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Tutorial on Practical Prediction Theory for Classification
- TUTORIAL ON PRACTICAL PREDICTION THEORY FOR CLASSIFICATION JOHN LANGFORD, IBM RESEARCH Abstract. We
- Cited by 34 (1 self) – Add To MetaCart
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PAC-MDL bounds
- PAC-MDL bounds Avrim Blum ?1 and John Langford 2 1 Computer Science Department, Carnegie Mellon
- Cited by 9 (0 self) – Add To MetaCart
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Microchoice Bounds and Self Bounding Learning Algorithms
- Microchoice Bounds and Self Bounding Learning Algorithms John Langford and Avrim Blum Computer
- Cited by 9 (0 self) – Add To MetaCart
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A comparison of tight generalization error bounds
- .kaariainen@(email omitted); International Computer Science Institute, 1947 Center St Suite 600, Berkeley, CA 94704, USA John Langford jl
- Cited by 3 (1 self) – Add To MetaCart
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Monte Carlo Hidden Markov Models
- Monte Carlo Hidden Markov Models Sebastian Thrun and John Langford December 1998 CMU-CS-98
- Cited by 7 (1 self) – Add To MetaCart
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Bounds for Averaging Classifiers
- Bounds for Averaging Classiers John Langford a Matthias Seeger b January 2001 CMU-CS-01-102 a
- Cited by 10 (4 self) – Add To MetaCart

