Knowledge Discovery Databases by Piatetsky Gregory (17 results)

Softcover. Condition: Very Good.

Paperback. Condition: Used: Very Good. Text unmarked, uncreased, gently used. Cover has slight shelfwear. Pages clean & bright, binding tight.*Ships Next Business day*.

Soft cover. Condition: Near Fine. First published 1991, Third printing, May 1993 per copy right page. Interior pages are clean and bright; binding is firm - no loose pages. No creases on spine - like new. There are no tears, writing or folds on inside pages. This is NOT an ex-lib and no remainder mark.…

Condition: as new. Cambridge, MA: MIT Press, 1991. Paperback. 539 pp.- Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called "discovery in databases." This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search.Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain?specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. English text. Condition : as new. Condition : as new copy. ISBN 9780262660709. Keywords : .…

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: Brand New. 539 pages. 9.50x7.25x1.25 inches. In Stock.

Condition: VG. 1993. Anaheim, California, July 14-15, 1991. 4to., 314pp., spiral wraps. VG, light wear.

Condition: VG. 1993. Eleventh National Conference on Artificial Intelligence, Washington, D.C. July 11-15, 1993. 4to., 336pp., spiral wraps. A few pen notes on one page. VG.

Condition: New. Print on Demand pp. 540.

Condition: New. Print on Demand pp. 540.

Condition: New. PRINT ON DEMAND pp. 540.

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorWilliam Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.KlappentextKnowledge Discovery in Databases brings t.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets.The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called discovery in databases. This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search.Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in.Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTE Laboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project. …

Taschenbuch. Condition: Neu. Knowledge Discovery in Databases | Gregory Piatetsky-Shapiro (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 1991 | MIT Press | EAN 9780262660709 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…