Synopsis
This book examines the design of an automated system for financial time series forecasting. It explores the level of automation which can be achieved by a system for modelling a given financial time series with the minimum of human intervention. It aims t
"synopsis" may belong to another edition of this title.
Synopsis
This text examines the design of an automated system for financial time series forecasting. It explores the level of automation which can be achieved by a system for modelling a given financial time series with the minimum of human intervention. It aims to help the reader understand the issues involved in setting neural network, or genetic algorithm parameters, and to develop methods to deal with the problems they raise in a practical manner.
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Intelligent Systems and Financial Forecasting (Perspectives in Neural Computing)
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Intelligent Systems and Financial Forecasting
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Intelligent Systems and Financial Forecasting (Perspectives in Neural Computing)
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Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 1997
ISBN 10: 3540760989
ISBN 13: 9783540760986
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Taschenbuch. Condition: Gut. 227 Seiten ex Library Book aus einer wissenschaftlichen Bibliothek Sprache: Englisch Gewicht in Gramm: 341. Seller Inventory # 277762
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Intelligent Systems and Financial Forecasting
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Condition: 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. Seller Inventory # 18740/2
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Intelligent Systems and Financial Forecasting
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Intelligent Systems and Financial Forecasting
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Springer London Apr 1997, 1997
ISBN 10: 3540760989
ISBN 13: 9783540760986
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 240 pp. Englisch. Seller Inventory # 9783540760986
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Intelligent Systems and Financial Forecasting
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Intelligent Systems and Financial Forecasting
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Intelligent Systems and Financial Forecasting (Perspectives in Neural Computing)
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