Graphical models : representations for learning, reasoning and data mining / Christian Borgelt, Matthias Steinbrecher & Rudolf Kruse.
Material type:
- text
- unmediated
- volume
- 9780470722107 (cloth)
- 047072210X (cloth)
- QA76.9.D343 B67 2009
Item type | Current library | Home library | Shelving location | Call number | Copy number | Status | Date due | Barcode | |
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Harare Institute of Technology Main Library | Harare Institute of Technology Main Library | General Collection | QA76.9.D343 BOR (Browse shelf(Opens below)) | 1 | Available | BK003088 |
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QA76.9.D32 JOH Managing time in relational databases: how to design, update and query temporal data / | QA76.9.D34 HAN Analyzing social media networks with NodeXL : insights from a connected world / | QA76.9.D343 Data mining methods / | QA76.9.D343 BOR Graphical models : representations for learning, reasoning and data mining / | QA76.9.D343 DAT Data mining : introductory and advanced topics / | QA76.9.D343 DUN Data mining : introductory and advanced topics / | QA76.9.D343 DUN Data mining : introductory and advanced topics / |
Includes bibliographical references and index.
1. Introduction
2. Imprecision and Uncertainty
3. Decomposition
4. Graphical Representation
5. Computing Projections
6. Naive Classifiers
7. Learning Global Structure
8. Learning Local Structure
9. Inductive Causation
10. Visualization
11. Applications
A. Proofs of Theorems
B. Software Tools
Graphical models are of increasing importance in applied statistics, and in particular in data mining. Providing a self-contained introduction and overview to learning relational, probabilistic, and possibilistic networks from data, this second edition of Graphical Models is thoroughly updated to include the latest research in this burgeoning field, including a new chapter on visualization. The text provides graduate students, and researchers with all the necessary background material, including modelling under uncertainty, decomposition of distributions, graphical representation of distribution
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