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Browsing Machine Learning and Inference Laboratory, College of Health and Human Services by Subject

Browsing Machine Learning and Inference Laboratory, College of Health and Human Services by Subject "data mining"

Now showing items 1-8 of 8

An Adjustable Description Quality Measure for Pattern Discovery in Large Databases Using the AQ Methodology

Kaufman, Kenneth A.; Michalski, Ryszard S. (2000-03)

In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no noise, then ...
The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide

Michalski, Ryszard S.; Kaufman, Kenneth A. (2000-03)

This report is a comprehensive user's guide for AQ18, an environment for natural induction, machine learning and knowledge discovery. By natural induction is meant a form of inductive inference which strives to induce data ...
Attributional Calculus: A Logic and Representation Language for Natural Induction

Michalski, Ryszard S. (2004-04)

Attributional calculus (AC) is a typed logic system that combines elements of propositional logic, predicate calculus, and multiple-valued logic for the purpose of natural induction. By natural induction is meant a form ...
Building Knowledge Scouts Using KGL Metalanguage

Michalski, Ryszard S.; Kaufman, Kenneth A. (2000)

Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation metalanguage, KGL, is used to ...
Generating Alternative Hypotheses in AQ Learning

Michalski, Ryszard S. (2004-12)

In many areas of application of machine learning and data mining, it is desirable to generate alternative inductive hypotheses from the given data. The Aq-ALT or, briefly, ALT method, presented in this paper, generates ...
Initial Considerations toward Knowledge Mining

Kaufman, Kenneth A.; Michalski, Ryszard S. (2004-10)

In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it in human-oriented forms. ...
Multitype Pattern Discovery Via AQ21: A Brief Description of the Method and Its Novel Features

Wojtusiak, Janusz; Michalski, Ryszard S.; Kaufman, Kenneth A.; Pietrzykowski, Jaroslaw (2006-06)

The AQ21 program seeks different types of patterns in data and represents them in human-oriented forms resembling natural language descriptions. Because of the latter feature it is called a natural induction program. This ...
Natural Induction and Conceptual Clustering: A Review of Applications

Michalski, Ryszard S.; Kaufman, Kenneth A.; Pietrzykowski, Jaroslaw; Wojtusiak, Janusz; Mitchell, Scott; Seeman, Doug (2006-06)

Natural induction and conceptual clustering are two methodologies pioneered by the GMU Machine Learning and Inference Laboratory for discovering conceptual relationships in data, and presenting them in the forms easy for ...

Browsing Machine Learning and Inference Laboratory, College of Health and Human Services by Subject "data mining"

Browsing Machine Learning and Inference Laboratory, College of Health and Human Services by Subject "data mining"

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