http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
representations for learning, reasoning and data mining
1 online resource (viii, 393 pages) : illustrations
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 distributi.
Graphical Models; Contents; Preface; 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; Bibliography; Index.
Christian Borgelt, Matthias Steinbrecher & Rudolf Kruse.
Includes bibliographical references and index.
Chichester, West Sussex, UK : John Wiley, 2009
Wiley series in computational statistics