Large-scale probabilistic predictors with and without guarantees of validity

被引：0

作者：

Vovk, Vladimir ^{[1
]}

Petej, Ivan ^{[1
]}

Fedorova, Valentina ^{[2
]}

机构：

[1] Univ London, Dept Comp Sci, Royal Holloway, Egham, Surrey, England

[2] Yandex, Moscow, Russia

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015) | 2015年 / 28卷

基金：

英国工程与自然科学研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies theoretically and empirically a method of turning machine-learning algorithms into probabilistic predictors that automatically enjoys a property of validity (perfect calibration) and is computationally efficient. The price to pay for perfect calibration is that these probabilistic predictors produce imprecise (in practice, almost precise for large data sets) probabilities. When these imprecise probabilities are merged into precise probabilities, the resulting predictors, while losing the theoretical property of perfect calibration, are consistently more accurate than the existing methods in empirical studies.

引用

页数：9

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