Neural Computation
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An Introduction to Variational Methods for Graphical Models
Machine Learning
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Gaussian Processes for Classification: Mean-Field Algorithms
Neural Computation
Bayesian Gaussian Process Classification with the EM-EP Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
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Gaussian process classifiers (GPCs) are a fully statistical model for kernel classification. We present a form of GPC which is robust to labeling errors in the data set. This model allows label noise not only near the class boundaries, but also far from the class boundaries which can result from mistakes in labelling or gross errors in measuring the input features. We derive an outlier robust algorithm for training this model which alternates iterations based on the EP approximation and hyperparameter updates until convergence. We show the usefulness of the proposed algorithm with model selection method through simulation results.