Bayesian regularization and pruning using a Laplace prior
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
On different facets of regularization theory
Neural Computation
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior
IEEE Transactions on Image Processing
A Bayesian Approach to Joint Feature Selection and Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
2005 Special Issue: Constructing Bayesian formulations of sparse kernel learning methods
Neural Networks - 2005 Special issue: IJCNN 2005
On Bayesian classification with Laplace priors
Pattern Recognition Letters
An adaptive penalized maximum likelihood algorithm
Signal Processing
Sparse Bayesian nonparametric regression
Proceedings of the 25th international conference on Machine learning
Laplace maximum margin Markov networks
Proceedings of the 25th international conference on Machine learning
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Nonlinear Feature Selection by Relevance Feature Vector Machine
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
A majorization-minimization algorithm for (multiple) hyperparameter learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On primal and dual sparsity of Markov networks
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors
IEEE Transactions on Image Processing
Probabilistic classification vector machines
IEEE Transactions on Neural Networks
Improved adaptive wavelet threshold for image denoising
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Point process models for spotting keywords in continuous speech
IEEE Transactions on Audio, Speech, and Language Processing
Maximum Entropy Discrimination Markov Networks
The Journal of Machine Learning Research
A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Sparse learning for support vector classification
Pattern Recognition Letters
A note on mean-field variational approximations in Bayesian probit models
Computational Statistics & Data Analysis
Estimating Haplotype Frequencies by Combining Data from Large DNA Pools with Database Information
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Automatic rank determination in projective nonnegative matrix factorization
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
The Journal of Machine Learning Research
Bayesian Generalized Kernel Mixed Models
The Journal of Machine Learning Research
Bayesian kernel projections for classification of high dimensional data
Statistics and Computing
A Bayesian Lasso via reversible-jump MCMC
Signal Processing
Smooth Bayesian kernel machines
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Compression and learning in linear regression
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Conditional infomax learning: an integrated framework for feature extraction and fusion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Fast sparse multinomial regression applied to hyperspectral data
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Blind separation of sparse sources using jeffrey’s inverse prior and the EM algorithm
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Sparse gaussian processes using backward elimination
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Bayesian model selection for logistic regression models with random intercept
Computational Statistics & Data Analysis
Relaxation of hard classification targets for LSE minimization
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Sparse multikernel support vector regression machines trained by active learning
Expert Systems with Applications: An International Journal
Recurrent sparse support vector regression machines trained by active learning in the time-domain
Expert Systems with Applications: An International Journal
Probit classifiers with a generalized Gaussian scale mixture prior
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
EP-GIG priors and applications in bayesian sparse learning
The Journal of Machine Learning Research
Probabilistic classifiers with a generalized Gaussian scale mixture prior
Pattern Recognition
Discovering factions in the computational linguistics community
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
Object recognition using sparse representation of overcomplete dictionary
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Dynamic learning of SCRF for feature selection and classification of hyperspectral imagery
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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The goal of supervised learning is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the "complexity" of the learned function. In Bayesian approaches, this is done by adopting a prior for the parameters of the function being learned. We propose a Bayesian approach to supervised learning, which leads to sparse solutions; that is, in which irrelevant parameters are automatically set exactly to zero. Other ways to obtain sparse classifiers (such as Laplacian priors, support vector machines) involve (hyper)parameters which control the degree of sparseness of the resulting classifiers; these parameters have to be somehow adjusted/estimated from the training data. In contrast, our approach does not involve any (hyper)parameters to be adjusted or estimated. This is achieved by a hierarchical-Bayes interpretation of the Laplacian prior, which is then modified by the adoption of a Jeffreys' noninformative hyperprior. Implementation is carried out by an expectation-maximization (EM) algorithm. Experiments with several benchmark data sets show that the proposed approach yields state-of-the-art performance. In particular, our method outperforms SVMs and performs competitively with the best alternative techniques, although it involves no tuning or adjustment of sparseness-controlling hyperparameters.