The Journal of Machine Learning Research
Sample compression bounds for decision trees
Proceedings of the 24th international conference on Machine learning
Multi-classification by categorical features via clustering
Proceedings of the 25th international conference on Machine learning
The Journal of Machine Learning Research
Cross-validation and bootstrapping are unreliable in small sample classification
Pattern Recognition Letters
Nearly Uniform Validation Improves Compression-Based Error Bounds
The Journal of Machine Learning Research
Importance weighted active learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
PAC-Bayesian learning of linear classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The offset tree for learning with partial labels
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Performance prediction for exponential language models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A tighter error bound for decision tree learning using PAC learnability
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed data mining: why do more than aggregating models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
COLT'07 Proceedings of the 20th annual conference on Learning theory
Selective sampling for classification
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Artificial Intelligence in Medicine
On the Foundations of Noise-free Selective Classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Learning with randomized majority votes
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Distribution-dependent PAC-bayes priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
A PAC-bayes bound for tailored density estimation
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Evaluating optimization algorithms: bounds on the performance of optimizers on unseen problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
The Journal of Machine Learning Research
The minimum volume covering ellipsoid estimation in kernel-defined feature spaces
ECML'06 Proceedings of the 17th European conference on Machine Learning
Margin-sparsity trade-off for the set covering machine
ECML'05 Proceedings of the 16th European conference on Machine Learning
Generalization error bounds using unlabeled data
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Unlabeled compression schemes for maximum classes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Bayesian hypothesis testing for pattern discrimination in brain decoding
Pattern Recognition
Representative prototype sets for data characterization and classification
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Tighter PAC-Bayes bounds through distribution-dependent priors
Theoretical Computer Science
Bayesian mixed-effects inference on classification performance in hierarchical data sets
The Journal of Machine Learning Research
PAC-bayes bounds with data dependent priors
The Journal of Machine Learning Research
Towards minimizing the annotation cost of certified text classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Identifying predictive hubs to condense the training set of $$k$$-nearest neighbour classifiers
Computational Statistics
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We discuss basic prediction theory and its impact on classification success evaluation, implications for learning algorithm design, and uses in learning algorithm execution. This tutorial is meant to be a comprehensive compilation of results which are both theoretically rigorous and quantitatively useful.There are two important implications of the results presented here. The first is that common practices for reporting results in classification should change to use the test set bound. The second is that train set bounds can sometimes be used to directly motivate learning algorithms.