An interference matching technique for inducing abstractions-Reference-Cited by-同舟云学术

An interference matching technique for inducing abstractions

Published:1978-05 Issue:5 Volume:21 Page:401-411
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Hayes-Roth Frederick¹,McDermott John²

Affiliation:

1. The RAND Corporation, Santa Monica, CA

2. Carnegie-Mellon Univ., Pittsburgh, PA

Abstract

A method for inducing knowledge by abstraction from a sequence of training examples is described. The proposed method, interference matching, induces abstractions by finding relational properties common to two or more exemplars. Three tasks solved by a program that uses an interference-matching algorithm are presented. Several problems concerning the description of the training examples and the adequacy of interference matching are discussed, and directions for future research are considered.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/359488.359503

Reference24 articles.

1. Bobrow D. and Winograd T. A knowledge representation language. Cognitive Science in press. Bobrow D. and Winograd T. A knowledge representation language. Cognitive Science in press.

2. A novel pattern learning and classification procedure applied to the learning of vowels

3. Hayes-Roth F. The role of partial and best matches in knowledge systems. In Pattern-Directed Inference Systems F. Hayes- Roth and D.A. Waterman Eds. Academic Press New York in press. Hayes-Roth F. The role of partial and best matches in knowledge systems. In Pattern-Directed Inference Systems F. Hayes- Roth and D.A. Waterman Eds. Academic Press New York in press.

Cited by 85 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On the cultivation of proper abstraction;E3S Web of Conferences;2023

2. Logical and Relational Learning;Cognitive Technologies;2008

3. A database clustering methodology and tool;Information Sciences;2005-03-04

4. How to Upgrade Propositional Learners to First Order Logic: A Case Study;Machine Learning and Its Applications;2001

5. How to Upgrade Propositional Learners to First Order Logic: A Case Study;Relational Data Mining;2001