The Equivalence of Support Vector Machine and Regularization Neural Networks
Neural Processing Letters
Error Functions for Prediction of Episodes of Poor Air Quality
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Non-retrieval: Blocking Pornographic Images
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
On different facets of regularization theory
Neural Computation
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
The subspace information criterion for infinite dimensional hypothesis spaces
The Journal of Machine Learning Research
A maximum entropy approach to species distribution modeling
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Operations Research
Hierarchical Bayesian Models for Regularization in Sequential Learning
Neural Computation
Second-Order Learning Algorithm with Squared Penalty Term
Neural Computation
International Journal of Computer Vision
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
Invariance priors for Bayesian feed-forward neural networks
Neural Networks
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
On one method of non-diagonal regularization in sparse Bayesian learning
Proceedings of the 24th international conference on Machine learning
Information Sciences: an International Journal
Using neural networks to model conditional multivariate densities
Neural Computation
Convergence of an online gradient algorithm with penalty for two-layer neural networks
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Generalization Error Estimation for Non-linear Learning Methods
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
A majorization-minimization algorithm for (multiple) hyperparameter learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Predicting MGMT Methylation Status of Glioblastomas from MRI Texture
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Pattern classification with class probability output network
IEEE Transactions on Neural Networks
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Probabilistic in silico prediction of protein-peptide interactions
RECOMB'05 Proceedings of the 2005 joint annual satellite conference on Systems biology and regulatory genomics
Expectation Propagation for microarray data classification
Pattern Recognition Letters
BEBI'08 Proceedings of the 1st WSEAS international conference on Biomedical electronics and biomedical informatics
Estimating predictive variances with kernel ridge regression
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
On h∞ filtering in feedforward neural networks training and pruning
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Regularisation techniques for conditional random fields: parameterised versus parameter-free
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
Predictive neuro-control of uncertain systems: design and use of a neuro-optimizer
Automatica (Journal of IFAC)
Sample complexity of linear learning machines with different restrictions over weights
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Logistic regression with weight grouping priors
Computational Statistics & Data Analysis
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Standard techniques for improved generalization from neuralnetworks include weight decay and pruning. Weight decay has aBayesian interpretation with the decay function corresponding to aprior over weights. The method of transformation groups and maximumentropy suggests a Laplace rather than a gaussian prior. Aftertraining, the weights then arrange themselves into two classes: (1)those with a common sensitivity to the data error and (2) thosefailing to achieve this sensitivity and that therefore vanish.Since the critical value is determined adaptively during training,pruning---in the sense of setting weights to exact zeros---becomesan automatic consequence of regularization alone. The count of freeparameters is also reduced automatically as weights are pruned. Acomparison is made with results of MacKay using the evidenceframework and a gaussian regularizer.