Communications of the ACM
Consonant recognition by modular construction of large phonemic time-delay neural networks
Advances in neural information processing systems 1
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A practical Bayesian framework for backpropagation networks
Neural Computation
Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension
Machine Learning - Special issue on computational learning theory
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Metric-Based Methods for Adaptive Model Selection and Regularization
Machine Learning
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Regularized principal manifolds
The Journal of Machine Learning Research
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Tutorial on Practical Prediction Theory for Classification
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Invariances in kernel methods: From samples to objects
Pattern Recognition Letters
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
Compression-Based Averaging of Selective Naive Bayes Classifiers
The Journal of Machine Learning Research
Backpropagation applied to handwritten zip code recognition
Neural Computation
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning
The Journal of Machine Learning Research
An Information Criterion for Variable Selection in Support Vector Machines
The Journal of Machine Learning Research
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
PAC-Bayesian learning of linear classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Particle Swarm Model Selection
The Journal of Machine Learning Research
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
The Journal of Machine Learning Research
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Embedded feature selection for support vector machines: state-of-the-art and future challenges
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Nyström approximate model selection for LSSVM
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
CoNet: feature generation for multi-view semi-supervised learning with partially observed views
Proceedings of the 21st ACM international conference on Information and knowledge management
Model selection based product kernel learning for regression on graphs
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Hi-index | 0.00 |
The principle of parsimony also known as "Ockham's razor" has inspired many theories of model selection. Yet such theories, all making arguments in favor of parsimony, are based on very different premises and have developed distinct methodologies to derive algorithms. We have organized challenges and edited a special issue of JMLR and several conference proceedings around the theme of model selection. In this editorial, we revisit the problem of avoiding overfitting in light of the latest results. We note the remarkable convergence of theories as different as Bayesian theory, Minimum Description Length, bias/variance tradeoff, Structural Risk Minimization, and regularization, in some approaches. We also present new and interesting examples of the complementarity of theories leading to hybrid algorithms, neither frequentist, nor Bayesian, or perhaps both frequentist and Bayesian!