Vendeur : Best Price, Torrance, CA, Etats-Unis
EUR 97,47
Quantité disponible : 2 disponible(s)
Ajouter au panierEtat : New. SUPER FAST SHIPPING.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 103,70
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 105,31
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
EUR 104,12
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 110,31
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 110,31
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 110,30
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : Chiron Media, Wallingford, Royaume-Uni
EUR 111,19
Quantité disponible : 10 disponible(s)
Ajouter au panierPaperback. Etat : New.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 154,13
Quantité disponible : 2 disponible(s)
Ajouter au panierPaperback. Etat : Brand New. 320 pages. 9.25x6.10x0.73 inches. In Stock.
Vendeur : preigu, Osnabrück, Allemagne
EUR 96,40
Quantité disponible : 5 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. Functional Networks with Applications | A Neural-Based Paradigm | Enrique Castillo (u. a.) | Taschenbuch | xi | Englisch | 2013 | Springer | EAN 9781461375623 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Edité par Springer US, Springer New York, 2013
ISBN 10 : 1461375622 ISBN 13 : 9781461375623
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 112,94
Quantité disponible : 1 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes.
Edité par Springer US, Springer US, 1998
ISBN 10 : 079238332X ISBN 13 : 9780792383321
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 114,36
Quantité disponible : 1 disponible(s)
Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Artificial neural networks have been recognized as a powerful tool to learn and reproduce systems in various fields of applications. Neural net works are inspired by the brain behavior and consist of one or several layers of neurons, or computing units, connected by links. Each artificial neuron receives an input value from the input layer or the neurons in the previ ous layer. Then it computes a scalar output from a linear combination of the received inputs using a given scalar function (the activation function), which is assumed the same for all neurons. One of the main properties of neural networks is their ability to learn from data. There are two types of learning: structural and parametric. Structural learning consists of learning the topology of the network, that is, the number of layers, the number of neurons in each layer, and what neurons are connected. This process is done by trial and error until a good fit to the data is obtained. Parametric learning consists of learning the weight values for a given topology of the network. Since the neural functions are given, this learning process is achieved by estimating the connection weights based on the given information. To this aim, an error function is minimized using several well known learning methods, such as the backpropagation algorithm. Unfortunately, for these methods: (a) The function resulting from the learning process has no physical or engineering interpretation. Thus, neural networks are seen as black boxes.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 169,92
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 160,56
Quantité disponible : 1 disponible(s)
Ajouter au panierHardcover. Etat : Like New. Like New. book.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 191,52
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
EUR 134,39
Quantité disponible : Plus de 20 disponibles
Ajouter au panierHardback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 666.