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Learning from data : artificial intelligence and statistics V

Author: Douglas H Fisher; Hans-Joachim Lenz
Publisher: New York : Springer, ©1996.
Series: Lecture notes in statistics (Springer-Verlag), v. 112.
Edition/Format:   eBook : Document : Conference publication : EnglishView all editions and formats
Database:WorldCat
Summary:
This volume contains a revised collection of papers originally presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics represented in this collection of 42 papers are diverse and include natural language applications, causality and graphical models, classification, learning, knowledge discovery, and exploratory data analysis. The papers illustrate the rich  Read more...
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Details

Genre/Form: Electronic books
Conference papers and proceedings
Congresses
Additional Physical Format: Print version:
Learning from data.
New York : Springer, ©1996
(DLC) 96011794
Material Type: Conference publication, Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Douglas H Fisher; Hans-Joachim Lenz
ISBN: 9781461224044 1461224047
OCLC Number: 828774958
Notes: "Papers presented at the Fifth International Workshop on Artificial Intelligence and Statistics, which was held at Ft. Lauderdale, Florida in January 1995"--Preface.
Description: 1 online resource (xii, 450 pages) : illustrations.
Contents: Causality --
Inference and Decision Making --
Search Control in Model Hunting --
Classication --
General Learning Issues --
EDA:Tools and Methods --
Decision and Regression Tree Induction --
Natural Language Processing.
Series Title: Lecture notes in statistics (Springer-Verlag), v. 112.
Responsibility: Doug Fisher, Hans-J. Lenz, editors.

Abstract:

This volume contains a revised collection of papers originally presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics represented in this collection of 42 papers are diverse and include natural language applications, causality and graphical models, classification, learning, knowledge discovery, and exploratory data analysis. The papers illustrate the rich possibilities for interdisciplinary study at the interface of artificial intelligence and statistics.

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