College of Public Health
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The College of Public Health provides education, research, and service opportunities to improve the health of our communities.
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Item A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems(1992-02-10) Bala, Jerzy W.; Bloedorn, Eric; De Jong, Kenneth A.; Kaufman, Kenneth A.; Michalski, Ryszard S.; Pachowicz, Peter W.; Vafaie, Haleh; Wnek, Janusz; Zhang, JianpingItem A Comparative Review of Selected Methods for Learning from Examples(1983) Dieterrich, Thomas G.; Michalski, Ryszard S.Item A Demographic-Economic Model for Adloescent Substance Abuse and Crime Prevention(2010-05-18T18:20:07Z) Cartwright, William; Kitsantas, Panagiota; Rose, StevenItem A Description of Preference Criterion in Constructive Learning: A Discussion of Basic Issues(1989-06) Zhang, Jianping; Michalski, Ryszard S.Item A General Criterion for Measuring Quality of Concept Descriptions(1988-10) Bergadano, Francesco; Matwin, Stan; Michalski, Ryszard S.; Zhang, JianpingItem A Geometrical Model for the Synthesis of Interval Covers(1971-06-24) Michalski, Ryszard S.Item A Knowledge Representation System Based on Dynamically Interlaced Hierarchies: Basic Ideas and Examples(1993-05) Hieb, Michael R.; Michalski, Ryszard S.Item A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP(2000-10) Kaufman, Kenneth A.; Michalski, Ryszard S.Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset. This paper describes briefly a method and a scripting language for developing knowledge scouts, and then reports on experiments with a knowledge scout, SCAMP, for discovering patterns characterizing relationships among lifestyles, symptoms and diseases in a large medical database. Discovered patterns are presented in two forms: (1) attributional rules, which are expressions in attributional calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. Preliminary results indicate a high potential utility of the presented methodology for deriving useful and understandable knowledge.Item A Logic-Based Approach to Conceptual Data Base Analysis(1983) Michalski, Ryszard S.; Baskin, Arthur B.; Spackman, Kent A.Item A Logic-based Approach to Conceptual Database Analysis(1982-11) Michalski, Ryszard S.; Baskin, Arthur B.; Spackman, Kent A.Item A Logic-Based Approach to Optimal Classification into a Large Number of Classes(1974-08) Michalski, Ryszard S.Item A Measure of Description Quality for Data Mining and its Implementation in the AQ18 Learning System(1999-06) Michalski, Ryszard S.; Kaufman, Kenneth A.Item A Method for Multistrategy Task-adaptive Learning Based on Plausible Justifications(1991-06) Tecuci, Gheorghe; Michalski, Ryszard S.Item A Method for Partial-Memory Incremental Learning and its Application to Computer Intrusion Detection(1995-11) Maloof, Marcus A.; Michalski, Ryszard S.Item A Method of Organizing Data into Conceptual Hierarchies(1981) Stepp, Robert E.; Michalski, Ryszard S.Item A Methodological Framework for Multistrategy Task-adaptive Learning(1990-10) Michalski, Ryszard S.Item A Partial Memory Incremental Learning Methodology and its Application to Computer Intrusion Detection(1995-03) Maloof, Marcus A.; Michalski, Ryszard S.Item A Planar Geometrical Model for Representing Multi-Dimensional Discrete Spaces and Multiple-Valued Logic Functions(1978-01) Michalski, Ryszard S.Item A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts(1981) Michalski, Ryszard S.; Stepp, Robert E.; Diday, EdwinItem A Rules-to-Trees Conversion in the Inductive Database System VINLEN(2005-06) Śnieżyński, Bartłomiej; Michalski, Ryszard S.Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an algorithm that converts rules to decision tree and its implementation in inductive database VINLEN is presented.