| 000 | 01203camuu2200325 a 4500 | |
| 001 | 000045584510 | |
| 005 | 20100408150746 | |
| 008 | 100405s2009 enka b 001 0 eng | |
| 010 | ▼a 2009028756 | |
| 020 | ▼a 047072210X (cloth) | |
| 020 | ▼a 9780470722107 (cloth) | |
| 035 | ▼a (OCoLC)ocn368051801 | |
| 040 | ▼a DLC ▼c DLC ▼d YDX ▼d BTCTA ▼d BWK ▼d BWX ▼d CDX ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a QA76.9.D343 ▼b B67 2009 |
| 082 | 0 0 | ▼a 006.3/12 ▼2 22 |
| 090 | ▼a 006.312 ▼b B732g2 | |
| 100 | 1 | ▼a Borgelt, Christian. |
| 245 | 1 0 | ▼a Graphical models : ▼b representations for learning, reasoning and data mining / ▼c Christian Borgelt, Matthias Steinbrecher & Rudolf Kruse. |
| 250 | ▼a 2nd ed. | |
| 260 | ▼a Chichester, West Sussex, UK : ▼b John Wiley , ▼c 2009. | |
| 300 | ▼a viii, 393 p. : ▼b ill. ; ▼c 24 cm. | |
| 490 | 1 | ▼a Wiley series in computational statistics |
| 504 | ▼a Includes bibliographical references and index. | |
| 650 | 0 | ▼a Mathematical statistics ▼x Graphic methods. |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Kruse, Rudolf. |
| 700 | 1 | ▼a Steinbrecher, Matthias. |
| 830 | 0 | ▼a Wiley series in computational statistics. |
| 945 | ▼a KLPA |
Holdings Information
| No. | Location | Call Number | Accession No. | Availability | Due Date | Make a Reservation | Service |
|---|---|---|---|---|---|---|---|
| No. 1 | Location Main Library/Western Books/ | Call Number 006.312 B732g2 | Accession No. 111574959 | Availability Available | Due Date | Make a Reservation | Service |
Contents information
Table of Contents
Preface.
1 Introduction.
1.1 Data and Knowledge.
1.2 Knowledge Discovery and Data Mining.
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.
3 Decomposition.
3.1 Decomposition and Reasoning.
3.2 Relational Decomposition.
3.3 Probabilistic Decomposition.
3.4 Possibilistic Decomposition.
3.5 Possibility versus Probability.
4 Graphical Representation.
4.1 Conditional Independence Graphs.
4.2 Evidence Propagation in Graphs.
5 Computing Projections.
5.1 Databases of Sample Cases.
5.2 Relational and Sum Projections.
5.3 Expectation Maximization.
5.4 Maximum Projections.
6 Naive Classifiers.
6.1 Naive Bayes Classifiers.
6.2 A Naive Possibilistic Classifier.
6.3 Classifier Simplification.
6.4 Experimental Evaluation.
7 Learning Global Structure.
7.1 Principles of Learning Global Structure.
7.2 Evaluation Measures.
7.3 Search Methods.
7.4 Experimental Evaluation.
8 Learning Local Structure.
8.1 Local Network Structure.
8.2 Learning Local Structure.
8.3 Experimental Evaluation.
9 Inductive Causation.
9.1 Correlation and Causation.
9.2 Causal and Probabilistic Structure.
9.3 Faithfulness and Latent Variables.
9.4 The Inductive Causation Algorithm.
9.5 Critique of the Underlying Assumptions.
9.6 Evaluation.
10 Visualization.
10.1 Potentials.
10.2 Association Rules.
11 Applications.
11.1 Diagnosis of Electrical Circuits.
11.2 Application in Telecommunications.
11.3 Application at Volkswagen.
11.4 Application at DaimlerChrysler.
A Proofs of Theorems.
A.1 Proof of Theorem 4.1.2.
A.2 Proof of Theorem 4.1.18.
A.3 Proof of Theorem 4.1.20.
A.4 Proof of Theorem 4.1.26.
A.5 Proof of Theorem 4.1.28.
A.6 Proof of Theorem 4.1.30.
A.7 Proof of Theorem 4.1.31.
A.8 Proof of Theorem 5.4.8.
A.9 Proof of Lemma .2.2.
A.10 Proof of Lemma .2.4.
A.11 Proof of Lemma .2.6.
A.12 Proof of Theorem 7.3.1.
A.13 Proof of Theorem 7.3.2.
A.14 Proof of Theorem 7.3.3.
A.15 Proof of Theorem 7.3.5.
A.16 Proof of Theorem 7.3.7.
B Software Tools.
Bibliography.
Index.
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