Anbieter: Anybook.com, Lincoln, Vereinigtes Königreich
EUR 6,75
Anzahl: 1 verfügbar
In den WarenkorbZustand: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,450grams, ISBN:9783540760986.
Sprache: Englisch
Verlag: Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 1997
ISBN 10: 3540760989 ISBN 13: 9783540760986
Anbieter: NEPO UG, Rüsselsheim am Main, Deutschland
Taschenbuch. Zustand: Gut. 227 Seiten ex Library Book aus einer wissenschaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 341.
Zustand: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | A fundamental objective of Artificial Intelligence (AI) is the creation of in telligent computer programs. In more modest terms AI is simply con cerned with expanding the repertoire of computer applications into new domains and to new levels of efficiency. The motivation for this effort comes from many sources. At a practical level there is always a demand for achieving things in more efficient ways. Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. Both of these desires are contained within AI and both provide the inspirational force behind its development. In terms of satisfying both of these desires there can be no better example than machine learning. Machines that can learn have an in-built effi ciency. The same software can be applied in many applications and in many circumstances. The machine can adapt its behaviour so as to meet the demands of new, or changing, environments without the need for costly re-programming. In addition, a machine that can learn can be ap plied in new domains with the genuine potential for innovation. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist. This last aspect is of major significance when considering machine learning as applied to fi nancial forecasting.
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes Königreich
EUR 114,33
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New. In.
EUR 92,27
Anzahl: Mehr als 20 verfügbar
In den WarenkorbZustand: New.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 150,41
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 227 pages. 9.50x6.50x0.75 inches. In Stock.
Taschenbuch. Zustand: Neu. Intelligent Systems and Financial Forecasting | Jason Kingdon | Taschenbuch | xii | Englisch | 1997 | Springer | EAN 9783540760986 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - A fundamental objective of Artificial Intelligence (AI) is the creation of in telligent computer programs. In more modest terms AI is simply con cerned with expanding the repertoire of computer applications into new domains and to new levels of efficiency. The motivation for this effort comes from many sources. At a practical level there is always a demand for achieving things in more efficient ways. Equally, there is the technical challenge of building programs that allow a machine to do something a machine has never done before. Both of these desires are contained within AI and both provide the inspirational force behind its development. In terms of satisfying both of these desires there can be no better example than machine learning. Machines that can learn have an in-built effi ciency. The same software can be applied in many applications and in many circumstances. The machine can adapt its behaviour so as to meet the demands of new, or changing, environments without the need for costly re-programming. In addition, a machine that can learn can be ap plied in new domains with the genuine potential for innovation. In this sense a machine that can learn can be applied in areas where little is known about possible causal relationships, and even in circumstances where causal relationships are judged not to exist. This last aspect is of major significance when considering machine learning as applied to fi nancial forecasting.