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PubMed · 11110131

Gaussian processes for classification: mean-field algorithms.

M Opper·O Winther
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Abstract

We derive a mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems. The theory also yields an approximate leave-one-out estimator for the generalization error, which is computed with no extra computational cost. We show that from the TAP approach, it is possible to derive both a simpler "naive" mean-field theory and support vector machines (SVMs) as limiting cases. For both mean-field algorithms and support vector machines, simulation results for three small benchmark data sets are presented. They show that one may get state-of-the-art performance by using the leave-one-out estimator for model selection and the built-in leave-one-out estimators are extremely precise when compared to the exact leave-one-out estimate. The second result is taken as strong support for the internal consistency of the mean-field approach.

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AlgorithmsAnimalsArtificial IntelligenceBayes TheoremBrachyuraClassificationComputer SimulationDiabetes Mellitus, Type 2Discrimination LearningFemaleGenetic Predisposition to DiseaseHumansIndians, North AmericanLikelihood FunctionsMaleModels, NeurologicalNormal DistributionSex CharacteristicsSound SpectrographyJournal ArticleResearch Support, Non-U.S. Gov't

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M Opper, O Winther. 2000. Gaussian processes for classification: mean-field algorithms.. https://doi.org/10.1162/089976600300014881

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