Neural Computation
Machine Learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Bayesian classification with Laplace priors
Pattern Recognition Letters
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
The Journal of Machine Learning Research
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods
The Journal of Machine Learning Research
Bayesian Inference for Sparse Generalized Linear Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
A novel hierarchical Bayesian HMM for multi-dimensional discrete data
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Extended linear models with Gaussian prior on the parameters and adaptive expansion vectors
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Selecting useful features for personal credit risk analysis
International Journal of Business Information Systems
Multi-class sparse Bayesian regression for neuroimaging data analysis
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Sparse bayesian learning for identifying imaging biomarkers in AD prediction
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Multiclass sparse Bayesian regression for fMRI-based prediction
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Single-frame image recovery using a Pearson type VII MRF
Neurocomputing
Probabilistic classifiers with a generalized Gaussian scale mixture prior
Pattern Recognition
Nested expectation propagation for Gaussian process classification
The Journal of Machine Learning Research
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In many real-world classification problems the input contains a large number of potentially irrelevant features. This paper proposes a new Bayesian framework for determining the relevance of input features. This approach extends one of the most successful Bayesian methods for feature selection and sparse learning, known as Automatic Relevance Determination (ARD). ARD finds the relevance of features by optimizing the model marginal likelihood, also known as the evidence. We show that this can lead to overfitting. To address this problem, we propose Predictive ARD based on estimating the predictive performance of the classifier. While the actual leave-one-out predictive performance is generally very costly to compute, the expectation propagation (EP) algorithm proposed by Minka provides an estimate of this predictive performance as a side-effect of its iterations. We exploit this in our algorithm to do feature selection, and to select data points in a sparse Bayesian kernel classifier. Moreover, we provide two other improvements to previous algorithms, by replacing Laplace's approximation with the generally more accurate EP, and by incorporating the fast optimization algorithm proposed by Faul and Tipping. Our experiments show that our method based on the EP estimate of predictive performance is more accurate on test data than relevance determination by optimizing the evidence.