Working Group Neural Networks and Fuzzy Systems
Graphical Models
Table of Contents
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1 Introduction
1.1 Data and Knowledge
1.2 Knowledge Discovery and Data Mining
1.2.1 The KDD Process
1.2.2 Data Mining Tasks
1.2.3 Data Mining Methods
1.3 Graphical Models
1.4 Outline of this Book
2 Imprecision and Uncertainty
2.1 Modeling Inferences
2.2 Imprecision and Relational Algebra
2.3 Uncertainty and Probability Theory
2.4 Possibility Theory and the Context Model
2.4.1 Experiments with Dice
2.4.2 The Context Model
2.4.3 The Insufficient Reason Principle
2.4.4 Overlapping Contexts
2.4.5 Mathematical Formalization
2.4.6 Normalization and Consistency
2.4.7 Possibility Measures
2.4.8 Mass Assignment Theory
2.4.9 Degrees of Possibility for Decision Making
2.4.10 Conditional Degrees of Possibility
2.4.11 Imprecision and Uncertainty
2.4.12 Open Problems
3 Decomposition
3.1 Decomposition and Reasoning
3.2 Relational Decomposition
3.2.1 A Simple Example
3.2.2 Reasoning in the Simple Example
3.2.3 Decomposability of Relations
3.2.4 Tuple-Based Formalization
3.2.5 Possibility-Based Formalization
3.2.6 Conditional Possibility and Independence
3.3 Probabilistic Decomposition
3.3.1 A Simple Example
3.3.2 Reasoning in the Simple Example
3.3.3 Factorization of Probability Distributions
3.3.4 Conditional Probability and Independence
3.4 Possibilistic Decomposition
3.4.1 Transfer from Relational Decomposition
3.4.2 A Simple Example
3.4.3 Reasoning in the Simple Example
3.4.4 Conditional Degrees of Possibility and Independence
3.5 Possibility versus Probability
4 Graphical Representation
4.1 Conditional Independence Graphs
4.1.1 Axioms of Conditional Independence
4.1.2 Graph Terminology
4.1.3 Separation in Graphs
4.1.4 Dependence and Independence Maps
4.1.5 Markov Properties of Graphs
4.1.6 Graphs and Decompositions
4.1.7 Markov Networks and Bayesian Networks
4.2 Evidence Propagation in Graphs
4.2.1 Propagation in Polytrees
4.2.2 Join Tree Propagation
4.2.3 Other Evidence Propagation Methods
5 Computing Projections
5.1 Databases of Sample Cases
5.2 Relational and Sum Projections
5.3 Expectation Maximization
5.4 Maximum Projections
5.4.1 A Simple Example
5.4.2 Computation via the Support
5.4.3 Computation via the Closure
5.4.4 Experimental Results
5.4.5 Limitations
6 Naive Classifiers
6.1 Naive Bayes Classifiers
6.1.1 The Basic Formula
6.1.2 Relation to Bayesian Networks
6.1.3 A Simple Example
6.2 A Naive Possibilistic Classifier
6.3 Classifier Simplification
6.4 Experimental Results
7 Learning Global Structure
7.1 Principles of Learning Global Structure
7.1.1 Learning Relational Networks
7.1.2 Learning Probabilistic Networks
7.1.3 Learning Possibilistic Networks
7.1.4 Components of a Learning Algorithm
7.2 Evaluation Measures
7.2.1 General Considerations
7.2.2 Notation and Presuppositions
7.2.3 Relational Evaluation Measures
7.2.4 Probabilistic Evaluation Measures
7.2.5 Possibilistic Evaluation Measures
7.3 Search Methods
7.3.1 Exhaustive Graph Search
7.3.2 Guided Random Search
7.3.3 Conditional Independence Search
7.3.4 Greedy Search
7.4 Experimental Results
7.4.1 Learning Probabilistic Networks
7.4.2 Learning Possibilistic Networks
8 Learning Local Structure
8.1 Local Network Structure
8.2 Learning Local Structure
8.3 Experimental Results
9 Inductive Causation
9.1 Correlation and Causation
9.2 Causal and Probabilistic Structure
9.3 Stability and Latent Variables
9.4 The Inductive Causation Algorithm
9.5 Critique of the Underlying Assumptions
9.6 Evaluation
10 Applications
10.1 Applications in Telecommunications
10.2 Application at Volkswagen
10.3 Application at DaimlerChrysler
A Proofs of Theorems
B Software Tools
Bibliography
Index
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© 2001
Christian Borgelt
Last modified: Fri Oct 25 11:03:33 MEST 2002