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Now showing items 178-197 of 310

Learning Patterns in Noisy Data: The AQ Approach

Michalski, Ryszard S.; Kaufman, Kenneth A. (2001)
Learning Rules for Preventing and Diagnosing Faults in Large-Scale Data Communications Networks: An Exploratory Study

Janssen, Terry; Bloedorn, Eric; Hieb, Michael R.; Michalski, Ryszard S. (1991-11)
Learning Strategies and Automated Knowledge Acquisition: An Overview

Michalski, Ryszard S. (1984-11)
Learning Symbolic Descriptions of 2D Shapes for Object Recognition in X-ray Images

Maloof, Marcus A.; Michalski, Ryszard S. (1995-10)
Learning Symbolic Descriptions of Shape for Object Recognition In X-Ray Images

Michalski, Ryszard S. (Elsevier, http://www.elsevier.com/locate/eswa, 1997)
Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results

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

This paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users’ interactions with computers. The signatures are in the form of collections ...
Learning Textural Concepts Through Multilevel Symbolic Transformations

Bala, Jerzy W.; Michalski, Ryszard S. (1991-11)
Learning to Predict Sequences

Dietterich, Tom G.; Michalski, Ryszard S. (1985-02)
Learning Two-Tiered Descriptions of Flexible Concepts: A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part I: Principles and Methodology

Bergadano, Francesco; Matwin, Stan; Michalski, Ryszard S.; Zhang, Jianping (1988-11)
Learning Two-Tiered Descriptions of Flexible Concepts: A Method Employing Examples of Varied Typicality and A Two-staged Construction of the Base Concept Representation Part II: Algorithms and Experiments

Bergadano, Francesco; Matwin, Stan; Michalski, Ryszard S.; Zhang, Jianping (1988-11)
Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System

Bergadano, Francesco; Matwin, Stan; Michalski, Ryszard S.; Zhang, Jianping (1990-09)
Learning Two-tiered Descriptions of Flexible Concepts: The POSEIDON System

Bergadano, Francesco; Matwin, Stan; Michalski, Ryszard S.; Zhang, Jianping (1991-05)
Learning User Models for Computer Intrusion Detection: Preliminary Results from Natural Induction Approach

Michalski, Ryszard S.; Kaufman, Kenneth A.; Pietrzykowski, Jaroslaw; Śnieżyński, Bartłomiej; Wojtusiak, Janusz (2005-11)

This paper presents a description of the LUS method for creating models (signatures) of computer users from datastreams that characterize users' interactions with computers, and the results of initial experiments with this ...
The LEM3 Implementation of Learnable Evolution Model and Its Testing on Complex Function Optimization Problems

Wojtusiak, Janusz; Michalski, Ryszard S. (2006-07)

Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new types of operators for creating new individuals, ...
The LEM3 System for Non-Darwinian Evolutionary Computation and Its Application to Complex Function Optimization

Wojtusiak, Janusz; Michalski, Ryszard S. (2005-10)

LEM3 is the newest implementation of Learnable Evolution Model (LEM), a non-Darwinian evolutionary computation methodology that employs machine learning to guide evolutionary processes. Due to a deep integration of different ...
The Logic of Plausible Reasoning: A Core Theory

Collins, Allan; Michalski, Ryszard S. (1988-11)
The Logic of Plausible Reasoning: An Advanced Report

Collins, Allan; Michalski, Ryszard S. (1986-02)
A Logic-Based Approach to Conceptual Data Base Analysis

Michalski, Ryszard S.; Baskin, Arthur B.; Spackman, Kent A. (1983)
A Logic-based Approach to Conceptual Database Analysis

Michalski, Ryszard S.; Baskin, Arthur B.; Spackman, Kent A. (1982-11)
A Logic-Based Approach to Optimal Classification into a Large Number of Classes

Michalski, Ryszard S. (1974-08)

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Browsing College of Health and Human Services by Title

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