Year: 1993
Type: article
Abstract: Many learning algorithms form concept descriptions composed of clauses, each of which covers some proportion of the positive training data and a small to zero proportion of the negative training data. This paper presents a method using likelihood ratios attached to clauses to classify test examples. One concept description is learned for each class. Each concept description competes to classify the test example using the likelihood ratios assigned to clauses of that concept description. By testing on several artificial and "real world" domains, we demonstrate that attaching weights and allowing concept descriptions to compete to classify examples reduces an algorithm's susceptibility to noise.
Authors: Kamal Ali, Michael J. Pazzani
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W176843356