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Massive Computing

By: Evangelos Triantaphyllou (Editor), Giovanni Felici (Editor)

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Hardcover | 21 June 2006

At a Glance

Hardcover
800 Pages

Dimensions(cm)
23.5 x 16.51 x 4.45

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2. Some Background Information 49 3. Definitions and Terminology 52 4. The One Clause at a Time (OCAT) Approach 54 4. 1 Data Binarization 54 4. 2 The One Clause at a Time (OCAT) Concept 58 4. 3 A Branch-and-Bound Approach for Inferring Clauses 59 4. 4 Inference of the Clauses for the Illustrative Example 62 4. 5 A Polynomial Time Heuristic for Inferring Clauses 65 5. A Guided Learning Approach 70 6. The Rejectability Graph of Two Collections of Examples 72 6. 1 The Definition of the Rej ectability Graph 72 6. 2 Properties of the Rejectability Graph 74 6. 3 On the Minimum Clique Cover of the Rej ectability Graph 76 7. Problem Decomposition 77 7. 1 Connected Components 77 7. 2 Clique Cover 78 8. An Example of Using the Rejectability Graph 79 9. Conclusions 82 References 83 Author's Biographical Statement 87 Chapter 3 AN INCREMENTAL LEARNING ALGORITHM FOR INFERRING LOGICAL RULES FROM EXAMPLES IN THE FRAMEWORK OF THE COMMON REASONING PROCESS, by X. Naidenova 89 1. Introduction 90 2. A Model of Rule-Based Logical Inference 96 2. 1 Rules Acquired from Experts or Rules of the First Type 97 2. 2 Structure of the Knowledge Base 98 2. 3 Reasoning Operations for Using Logical Rules of the First Type 100 2. 4 An Example of the Reasoning Process 102 3. Inductive Inference of Implicative Rules From Examples 103 3.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Mathematics Optimisation Linear Programming Computer Science Artificial Intelligence Machine Learning Business & Management Operational Research

Management & Management Techniques Management Decision Making

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Published: 11th February 2011

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List of Figures
List of Tables
Foreword
Preface
Acknowledgements
A Common Logic Approach to Data Mining and Pattern Recognition
The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery
An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process
Discovering Rules that Govern Monotone Phenomena
Learning Logic Formulas and Related Error Distributions
Feature Selection for Data Mining
Transformation of Rational and Set Data to Logic Data
Data Farming: Concepts and Methods
Rule Induction Through Discrete Support Vector Decision Trees
Multi-Attribute Decision Trees and Decision Rules
Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective
Discovering Knowledge Nuggets with a Genetic Algorithm
Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems
Fuzzy Logic in Discovering Association Rules: An Overview
Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview
Data Mining from Multimedia Patient Records
Learning to Find Context Based Spelling Errors
Induction and Inference with Fuzzy Rules for Textual Information Retrieval
Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem
Future Trends in Some Data Mining Areas
Subject Index
Author Index
Contributor Index
About the Editors
Table of Contents provided by Publisher. All Rights Reserved.

Massive Computing

At a Glance

Hardcover

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How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

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