Iterative rule simplification for noise tolerant inductive learning

Author

Pachowicz, Peter W. ; Bala, Jerzy ; Zhang, Jianping

Author_Institution

Center for Artificial Intelligence, George Mason Univ., Fairfax, VA, USA

fYear

1992

fDate

10-13 Nov 1992

Firstpage

452

Lastpage

453

Abstract

An iterative noise reduction learning algorithm is presented in which rules are learned in two phases. The first phase improves the quality of training data through a concept-driven closed-loop filtration process. In the second phase, classification rules are relearned from the filtered training data set

Keywords

knowledge acquisition; learning (artificial intelligence); classification rules; concept-driven closed-loop filtration process; iterative noise reduction learning algorithm; noise tolerant inductive learning; training data; Algorithm design and analysis; Artificial intelligence; Computer science; Filtration; Iterative algorithms; Learning systems; Noise reduction; Phase noise; System testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1992. TAI '92, Proceedings., Fourth International Conference on

Conference_Location

Arlington, VA

Print_ISBN

0-8186-2905-3

Type

conf

DOI

10.1109/TAI.1992.246447

Filename

246447

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3377163