Information-based objective functions for active data selection
Neural Computation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Compressed sensing and Bayesian experimental design
Proceedings of the 25th international conference on Machine learning
Active learning and basis selection for kernel-based linear models: a Bayesian perspective
IEEE Transactions on Signal Processing
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
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In this paper we present an active learning procedure for the two-class supervised classification problem. The utilized methodology exploits the Bayesian modeling and inference paradigm to tackle the problem of kernel-based data classification. This Bayesian methodology is appropriate for both finite and infinite dimensional feature spaces. Parameters are estimated, using the kernel trick, following the evidence Bayesian approach from the marginal distribution of the observations. The proposed active learning procedure uses a criterion based on the entropy of the posterior distribution of the adaptive parameters to select the sample to be included in the training set. A synthetic dataset as well as a real remote sensing classification problem are used to validate the followed approach.