rzempoluck (33 risultati)

Hardcover. Condizione: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condizione: Sehr gut. 236 p. Including CD-ROM. In very good condition. ISBN: 9780387982557 Sprache: Englisch Gewicht in Gramm: 642 17,8 x 1,4 x 23,5 cm, hardcover.

Condizione: New.

Condizione: Used. pp. viii + 226.

Condizione: Used. pp. viii + 226.

Condizione: Used. pp. viii + 226.

Condizione: New.

Mixed Media Product. Condizione: New. Neural Network Data Analysis Using Simulnet(tm) [With CDROM]. Media.

Condizione: As New. Unread book in perfect condition.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In.

Condizione: New. In.

Paperback. Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: Sehr gut. Auflage: 1998. 236 Seiten IN ENGLISH Exemplar aus einer wissenchaftlichen Bibliothek Altersfreigabe FSK ab 0 Jahre Sprache: Englisch Gewicht in Gramm: 969 24,4 x 18,4 x 2,2 cm, Gebundene Ausgabe.

Paperback. Condizione: Brand New. 234 pages. 11.00x8.27x0.57 inches. In Stock.

Hardcover. Condizione: Brand New. hardback/cd-rom edition. 226 pages. 9.75x7.50x0.75 inches. In Stock.

Condizione: Gut. Zustand: Gut | Sprache: Englisch | Produktart: Bücher | Scope of this Text This text is intended to provide the reader with an introduction to the analysis of numeri­ cal data using neural networks. Neural networks as data analytic tools allow data to be analyzed in order to discover and model the functional relationships among the recorded variables. Such data may be empirical. It may originate in an experiment in which the values of one or more dependent variables are recorded as one or more independent vari­ ables are manipulated. Alternatively, the data may be observational rather than empirical in nature, representing historical records of the behavior of some set of variables. An ex­ ample would be the values of a number of financial commodities, such as stocks or bonds. Finally, the data may originate in a computational model of some physical proc­ ess. Instead of recording variables of the physical process, the computer model could be run to generate an artificial analog of the physical data. Since data in virtually any native form can be expressed in numerical format, the scope of the analytical techniques and procedures that will be presented in this text is es­ sentially unlimited. Sources of data include research work in a range of disciplines as di­ verse as neuroscience, biomedicine, geophysics, psychology, sociology, archeology, eco­ nomics, and astrophysics. An often fruitful approach to data analysis involves the use of neural network func­ tions.

Taschenbuch. Condizione: Neu. Neural Network Data Analysis Using Simulnet(TM) | Edward J. Rzempoluck | Taschenbuch | viii | Englisch | 2013 | Springer | EAN 9781461272625 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Scope of this Text This text is intended to provide the reader with an introduction to the analysis of numeri cal data using neural networks. Neural networks as data analytic tools allow data to be analyzed in order to discover and model the functional relationships among the recorded variables. Such data may be empirical. It may originate in an experiment in which the values of one or more dependent variables are recorded as one or more independent vari ables are manipulated. Alternatively, the data may be observational rather than empirical in nature, representing historical records of the behavior of some set of variables. An ex ample would be the values of a number of financial commodities, such as stocks or bonds. Finally, the data may originate in a computational model of some physical proc ess. Instead of recording variables of the physical process, the computer model could be run to generate an artificial analog of the physical data. Since data in virtually any native form can be expressed in numerical format, the scope of the analytical techniques and procedures that will be presented in this text is es sentially unlimited. Sources of data include research work in a range of disciplines as di verse as neuroscience, biomedicine, geophysics, psychology, sociology, archeology, eco nomics, and astrophysics. An often fruitful approach to data analysis involves the use of neural network func tions.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Scope of this Text This text is intended to provide the reader with an introduction to the analysis of numeri cal data using neural networks. Neural networks as data analytic tools allow data to be analyzed in order to discover and model the functional relationships among the recorded variables. Such data may be empirical. It may originate in an experiment in which the values of one or more dependent variables are recorded as one or more independent vari ables are manipulated. Alternatively, the data may be observational rather than empirical in nature, representing historical records of the behavior of some set of variables. An ex ample would be the values of a number of financial commodities, such as stocks or bonds. Finally, the data may originate in a computational model of some physical proc ess. Instead of recording variables of the physical process, the computer model could be run to generate an artificial analog of the physical data. Since data in virtually any native form can be expressed in numerical format, the scope of the analytical techniques and procedures that will be presented in this text is es sentially unlimited. Sources of data include research work in a range of disciplines as di verse as neuroscience, biomedicine, geophysics, psychology, sociology, archeology, eco nomics, and astrophysics. An often fruitful approach to data analysis involves the use of neural network func tions.

Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Scope of this Text This text is intended to provide the reader with an introduction to the analysis of numeri­ cal data using neural networks. Neural networks as data analytic tools allow data to be analyzed in order to discover and model the functional relationships among the recorded variables. Such data may be empirical. It may originate in an experiment in which the values of one or more dependent variables are recorded as one or more independent vari­ ables are manipulated. Alternatively, the data may be observational rather than empirical in nature, representing historical records of the behavior of some set of variables. An ex­ ample would be the values of a number of financial commodities, such as stocks or bonds. Finally, the data may originate in a computational model of some physical proc­ ess. Instead of recording variables of the physical process, the computer model could be run to generate an artificial analog of the physical data. Since data in virtually any native form can be expressed in numerical format, the scope of the analytical techniques and procedures that will be presented in this text is es­ sentially unlimited. Sources of data include research work in a range of disciplines as di­ verse as neuroscience, biomedicine, geophysics, psychology, sociology, archeology, eco­ nomics, and astrophysics. An often fruitful approach to data analysis involves the use of neural network func­ tions.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets. 240 pp. Englisch.

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation.

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets. 240 pp. Englisch.