Precise induction from statistical data

被引：0

作者：

Schweitzer, H ^{[1
]}

机构：

[1] Univ Texas, Richardson, TX 75083 USA

来源：

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE | 1999年 / 11卷 / 02期

关键词：

induction; data-mining; machine-learning; PAC-learning;

D O I：

10.1080/095281399146535

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In inductive reasoning one uses a small set of examined instances to infer global relations. The standard approach is to search for relations that can be verified in all the examined instances, and hypothesize that they hold globally Relations that hold only for a subset of the examined instances were previously used only for statistical inference. In this paper it is shown that this statistical information can also be used to infer relations that hold for all instances. The main result is an algorithm that uses statistics to infer Boolean predicates. The analysis includes an investigation of what statistics are relevant for such inference, and what predicates can be inferred.

引用

页码：185 / 199

页数：15

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