Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
The Strength of Weak Learnability
Machine Learning
A maximum entropy approach to natural language processing
Computational Linguistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
A geometric approach to leveraging weak learners
Theoretical Computer Science
Strong Entropy Concentration, Game Theory, and Algorithmic Randomness
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
New developments in parsing technology
Totally corrective boosting algorithms that maximize the margin
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Surrogate maximization/minimization algorithms and extensions
Machine Learning
Sketching information divergences
Machine Learning
Surrogate maximization/minimization algorithms and extensions
Machine Learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Herding dynamical weights to learn
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bregman divergences in the (m×k)-partitioning problem
Computational Statistics & Data Analysis
Sided and symmetrized Bregman centroids
IEEE Transactions on Information Theory
Sketching information divergences
COLT'07 Proceedings of the 20th annual conference on Learning theory
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
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