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Discussion sur les prédicteurs conformes proposés par Alex Gammerman et Vladimir Vovk. Alexey Chervonenkis. La revue MODULAD, numéro 42, 2010.
Mots clés
Apprentissage automatique, prédicteurs conformes, complexité de Kolmogorov, approche bayésienne, étrangeté d’une prédiction
Abstract
Conformer
predictors approach seems to be new and powerful. Its main advantage is
that it is nonparametric and based only on the i.i.d. assumption. In
comparison to the Bayesian approach, no prior distribution is used. The
main theoretical result is the proof of validity of proposed conformal
predictors. The second result is that asymptotically the relative
number of cases when the real output value is within confidence
interval converges to the average value of conformal predictors. The
proposed technique is now applied to a large variety of practical
problems. Two drawbacks of the approach are still mentioned in this
discussion
Key words
Machine Learning, conformer predictor, Kolmogorov complexity, Bayesian approach, prediction strangeness.
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