Title
Discovery of Empirical Theories Based on the Measurement Theory
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
11-2004
Abstract
The purpose of this work is to analyse the cognitive process of the domain theories in terms of the measurement theory to develop a computational machine learning approach for implementing it. As a result, the relational data mining approach, the authors proposed in the preceding books, was improved. We present the approach as an implementation of the cognitive process as the measurement theory perceived. We analyse the cognitive process in the first part of the paper and present the theory and method of the logically most powerful empirical theory discovery in the second. The theory is based on the notion of ‘law-like’ rules, which conform to all the properties of laws of nature, namely generality, simplicity, maximum refutability and minimum number of parameters. This notion is defined for deterministic and probabilistic cases. Based on the method, the ‘discovery’ system is developed. The system was successfully applied to many practical tasks.
Recommended Citation
Vityaev, E. E., & Kovalerchuk, B. Y. (2004). Discovery of Empirical Theories Based on the Measurement Theory. Minds and Machines, 14(4), 551–573. https://doi.org/10.1023/b:mind.0000045991.67908.13
Journal
Minds and Machines
Rights
© 2004 Kluwer Academic Publishers
Comments
This article was originally published in Minds and Machines. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.