STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Journal of the ACM (JACM)
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Improved lower bounds for learning from noisy examples: an information-theoretic approach
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Prequential and Cross-Validated Regression Estimation
Machine Learning
Theoretical analysis of a class of randomized regularization methods
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
An approach to guaranteeing generalisation in neural networks
Neural Networks
Query by committee, linear separation and random walks
Theoretical Computer Science
Query by Committee, Linear Separation and Random Walks
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Bounds on the Generalization Ability of Bayesian Inference and Gibbs Algorithms
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Distinctive Features of Minimization of a Risk Functional in Mass Data Sets
Cybernetics and Systems Analysis
Predictability, Complexity, and Learning
Neural Computation
QG/GA: a stochastic search for Progol
Machine Learning
Classification and Retrieval through Semantic Kernels
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Boosting Active Learning to Optimality: A Tractable Monte-Carlo, Billiard-Based Algorithm
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
Estimating the size of neural networks from the number of available training data
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Classification and reductio-ad-absurdum optimality proofs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Bayesian active learning using arbitrary binary valued queries
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
A general system for automatic biomedical image segmentation using intensity neighborhoods
Journal of Biomedical Imaging
A hybrid bayesian optimal classifier based on neuro-fuzzy logic
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Artificial Intelligence in Medicine
A theory of transfer learning with applications to active learning
Machine Learning
Learning theory approach to minimum error entropy criterion
The Journal of Machine Learning Research
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In this paper we study a Bayesian or average-case model of concept learning with a twofold goal: to provide more precise characterizations of learning curve (sample complexity) behavior that depend on properties of both the prior distribution over concepts and the sequence of instances seen by the learner, and to smoothly unite in a common framework the popular statistical physics and VC dimension theories of learning curves. To achieve this, we undertake a systematic investigation and comparison of two fundamental quantities in learning and information theory: the probability of an incorrect prediction for an optimal learning algorithm, and the Shannon information gain. This study leads to a new understanding of the sample complexity of learning in several existing models.