Nonlinear statistical models
Measuring the VC-dimension of a learning machine
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
An experimental and theoretical comparison of model selection methods
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Performance bounds for nonlinear time series prediction
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Self bounding learning algorithms
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Technometrics
Improved Generalization Through Explicit Optimization of Margins
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A sharp concentration inequality with application
Random Structures & Algorithms
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Concept learning using complexity regularization
IEEE Transactions on Information Theory
Minimum complexity regression estimation with weakly dependent observations
IEEE Transactions on Information Theory - Part 2
An asymptotic property of model selection criteria
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Nonparametric estimation via empirical risk minimization
IEEE Transactions on Information Theory
Radial basis function networks and complexity regularization in function learning
IEEE Transactions on Neural Networks
Adaptive Model Selection for Digital Linear Classifiers
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Automatic Hyperparameter Tuning for Support Vector Machines
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Data-Dependent Margin-Based Generalization Bounds for Classification
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Localized Rademacher Complexities
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
An introduction to boosting and leveraging
Advanced lectures on machine learning
On learning multicategory classification with sample queries
Information and Computation
Quantum optimization for training support vector machines
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Data-dependent margin-based generalization bounds for classification
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Greedy algorithms for classification—consistency, convergence rates, and adaptivity
The Journal of Machine Learning Research
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
Asymptotics in Empirical Risk Minimization
The Journal of Machine Learning Research
Theoretical Computer Science - Computing and combinatorics
Model selection by bootstrap penalization for classification
Machine Learning
The Journal of Machine Learning Research
Estimation of the conditional risk in classification: The swapping method
Computational Statistics & Data Analysis
Co-occurrence Matrixes for the Quality Assessment of Coded Images
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning from Multiple Sources
The Journal of Machine Learning Research
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Hybrid Neural Systems for Reduced-Reference Image Quality Assessment
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Maximal-discrepancy bounds for regularized classifiers
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Model selection with the Loss Rank Principle
Computational Statistics & Data Analysis
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
IEEE Transactions on Neural Networks
Quantization and clustering with Bregman divergences
Journal of Multivariate Analysis
Mixing linear SVMs for nonlinear classification
IEEE Transactions on Neural Networks
Rademacher Complexities and Bounding the Excess Risk in Active Learning
The Journal of Machine Learning Research
PAC-Bayesian Analysis of Co-clustering and Beyond
The Journal of Machine Learning Research
Maximal Discrepancy for Support Vector Machines
Neurocomputing
A novel multi-view classifier based on Nyström approximation
Expert Systems with Applications: An International Journal
Data mining tools: from web to grid architectures
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
An alternative approach to avoid overfitting for surrogate models
Proceedings of the Winter Simulation Conference
Approximation and estimation bounds for free knot splines
Computers & Mathematics with Applications
Eigenvalues perturbation of integral operator for kernel selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Universal learning using free multivariate splines
Neurocomputing
Generalization ability of fractional polynomial models
Neural Networks
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers
Pattern Recognition Letters
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We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.