A framework for structural risk minimisation

A PAC analysis of a Bayesian estimator

COLT '97 Proceedings of the tenth annual conference on Computational learning theory

Some PAC-Bayesian theorems

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory

Self bounding learning algorithms

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory

Microchoice bounds and self bounding learning algorithms

COLT '99 Proceedings of the twelfth annual conference on Computational learning theory

Some PAC-Bayesian Theorems

Machine Learning - The Eleventh Annual Conference on computational Learning Theory

A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery

Boosting as a Monte Carlo Algorithm

AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence

Generalization Performance of Classifiers in Terms of Observed Covering Numbers

EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory

Reverse testing: an efficient framework to select amongst classifiers under sample selection bias

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Support vector machines for dyadic data

Neural Computation

Large margin cost-sensitive learning of conditional random fields

Pattern Recognition

Sequence classification via large margin hidden Markov models

Data Mining and Knowledge Discovery

Sets of approximating functions with finite Vapnik-Chervonenkis dimension for nearest-neighbors algorithms

Pattern Recognition Letters

Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks

International Journal of Applied Mathematics and Computer Science

Constructing multiclass learners from binary learners: a simple black-box analysis of the generalization errors

ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory

Letters: Dynamic classifier ensemble using classification confidence

Neurocomputing

Semi-supervised learning of hidden conditional random fields for time-series classification

Neurocomputing