causal models intelligent data (12 Ergebnisse)

Hardcover. Zustand: Sehr gut. X, 185 S.; Ill. Very good. Shrink wrapped. Sprache: Englisch Gewicht in Gramm: 545.

Hardcover/Pappeinband. Zustand: Sehr gut. 195 p. Very good. Shrink wrapped. / Sehr guter Zustand. In Folie verschweißt. Sprache: Englisch Gewicht in Gramm: 447.

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hardcover. Zustand: Good. Ex-library copy with stamps and stickers. The pages are lightly tanned. The copy shows minor external wear, but is in otherwise clean condition.

Gebunden. Zustand: Gut. Gebraucht - Gut Zustand: Gut, Mängelexemplar, X, 185 pp. 27 figs., 13 tabs. Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods. This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area. Written for professionals, students, and researchers.

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new computational methods. This book presents new intelligent data management methods and tools, including new results from the field of inference. Leading experts also map out future directions of intelligent data analysis. This book will be a valuable reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods.This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.

Taschenbuch. Zustand: Neu. Causal Models and Intelligent Data Management | Alex Gammerman | Taschenbuch | x | Englisch | 2012 | Springer-Verlag GmbH | EAN 9783642636820 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | Data analysis and inference have traditionally been research areas of statistics. However, the need to electronically store, manipulate and analyze large-scale, high-dimensional data sets requires new methods and tools, new types of databases, new efficient algorithms, new data structures, etc. - in effect new computational methods.This monograph presents new intelligent data management methods and tools, such as the support vector machine, and new results from the field of inference, in particular of causal modeling. In 11 well-structured chapters, leading experts map out the major tendencies and future directions of intelligent data analysis. The book will become a valuable source of reference for researchers exploring the interdisciplinary area between statistics and computer science as well as for professionals applying advanced data analysis methods in industry and commerce. Students and lecturers will find the book useful as an introduction to the area.