More theorems about scale-sensitive dimensions and learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
The complexity of learning according to two models of a drifting environment
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Complexity of Learning According to Two Models of a Drifting Environment
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Improved bounds on the sample complexity of learning
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Nonparametric Time Series Prediction Through Adaptive ModelSelection
Machine Learning
Simulation in exponential families
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Generalization Performances of Perceptrons
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Distribution-Dependent Vapnik-Chervonenkis Bounds
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Using the Pseudo-Dimension to Analyze Approximation Algorithms for Integer Programming
WADS '01 Proceedings of the 7th International Workshop on Algorithms and Data Structures
Entropy, Combinatorial Dimensions and Random Averages
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
A few notes on statistical learning theory
Advanced lectures on machine learning
On the size of convex hulls of small sets
The Journal of Machine Learning Research
Covering numbers, vapnik-červonenkis classes and bounds for the star-discrepancy
Journal of Complexity
Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks
Neural Computation
Bounds and constructions for the star-discrepancy via δ-covers
Journal of Complexity
Approximation by neural networks and learning theory
Journal of Complexity - Special issue: Algorithms and complexity for continuous problems Schloss Dagstuhl, Germany, September 2004
Model selection by bootstrap penalization for classification
Machine Learning
On the complexity of constrained VC-classes
Discrete Applied Mathematics
Aspects of discrete mathematics and probability in the theory of machine learning
Discrete Applied Mathematics
Bracketing numbers for axis-parallel boxes and applications to geometric discrepancy
Journal of Complexity
Finite-Time Bounds for Fitted Value Iteration
The Journal of Machine Learning Research
Constrained versions of Sauer's lemma
Discrete Applied Mathematics
Shifting: One-inclusion mistake bounds and sample compression
Journal of Computer and System Sciences
Using the doubling dimension to analyze the generalization of learning algorithms
Journal of Computer and System Sciences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Bounds and constructions for the star-discrepancy via δ-covers
Journal of Complexity
Approximation by neural networks and learning theory
Journal of Complexity - Special issue: Algorithms and complexity for continuous problems Schloss Dagstuhl, Germany, September 2004
One-inclusion hypergraph density revisited
Information Processing Letters
Journal of Computer and System Sciences
Optimal adaptive sampling recovery
Advances in Computational Mathematics
Comparing distributions and shapes using the kernel distance
Proceedings of the twenty-seventh annual symposium on Computational geometry
Covering numbers, dyadic chaining and discrepancy
Journal of Complexity
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Optimal private halfspace counting via discrepancy
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Brief Finite sample properties of system identification of ARX models under mixing conditions
Automatica (Journal of IFAC)
A geometric approach to sample compression
The Journal of Machine Learning Research
Universal learning using free multivariate splines
Neurocomputing
Generalization ability of fractional polynomial models
Neural Networks
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