Graphical models : representations for learning, reasoning and data mining / Christian Borgelt, Matthias Steinbrecher & Rudolf Kruse.

By:

Borgelt, Christian

Contributor(s):

Material type: Text

TextLanguage: Eng Series: Publication details: Chichester, West Sussex, UK : John Wiley, 2009.Edition: 2nd edDescription: viii, 393 pages : illustrations ; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780470722107 (cloth)
047072210X (cloth)

Subject(s):

LOC classification:

QA76.9.D343 B67 2009

Contents:

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

Summary: 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

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Home library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Books	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

Browsing Harare Institute of Technology Main Library shelves, Shelving location: General Collection Close shelf browser (Hides shelf browser)

Previous								Next
Previous	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 /	Next

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

There are no comments on this title.

to post a comment.