Machine Learning from examples: Inductive and Lazy methods

被引：54

作者：

de Mantaras, RL ^{[1
]}

Armengol, E ^{[1
]}

机构：

[1] CSIC, Artificial Intelligence Res Inst, Bellaterra 08193, Spain

来源：

DATA & KNOWLEDGE ENGINEERING | 1998年 / 25卷 / 1-2期

关键词：

Machine Learning; Inductive learning; Inductive Logic Programming; Lazy learning; instance-based learning; case-based reasoning;

D O I：

10.1016/S0169-023X(97)00053-0

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. Inductive learning methods are typically used to acquire general knowledge from examples. Lazy methods are those in which the experience is accessed, selected and used in a problem-centered way. In this paper we report important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in Inductive Logic Programming based methods, as well as to lazy methods such as instance-based and case-based reasoning. (C) 1998 Elsevier Science B.V.

引用

页码：99 / 123

页数：25