Complex-Valued Neural Networks with Multi-Valued Neurons (Registro nro. 14264)
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| 000 -CABECERA | |
|---|---|
| Campo de control de longitud fija | 03045nam a2200265za04500 |
| 001 - NÚMERO DE CONTROL | |
| Campo de control | 17639 |
| 008 - CAMPO FIJO DE DESCRIPCIÓN FIJA--INFORMACIÓN GENERAL | |
| Campo de control de longitud fija | 050703s2011 gw eng d |
| 020 ## - ISBN (INTERNATIONAL STANDARD BOOK NUMBER) | |
| ISBN | 9783642203534 99783642203534 |
| 082 ## - NÚMERO DE LA CLASIFICACIÓN DECIMAL DEWEY | |
| Número de clasificación Decimal | 006.3 |
| Número de documento (Cutter) | 223 |
| 100 ## - ENCABEZAMIENTO PRINCIPAL--NOMBRE PERSONAL | |
| Nombre de persona | Aizenberg, Igor. |
| Término relacionador | author. |
| 9 (RLIN) | 34300 |
| 245 ## - TÍTULO PROPIAMENTE DICHO | |
| Título | Complex-Valued Neural Networks with Multi-Valued Neurons |
| Medio físico | [electronic resource] / |
| Mención de responsabilidad, etc. | by Igor Aizenberg. |
| 300 ## - DESCRIPCIÓN FÍSICA | |
| Extensión | XVI, 264p. 258 illus. |
| Otros detalles físicos | online resource. |
| 490 ## - MENCIÓN DE SERIE | |
| Mención de serie | Studies in Computational Intelligence |
| 490 ## - MENCIÓN DE SERIE | |
| ISSN | -1860-949X ; |
| Número de volumen/designación secuencial | -353 |
| 505 ## - NOTA DE CONTENIDO FORMATEADA | |
| Nota de contenido con formato preestablecido | Why We Need Complex-Valued Neural Networks? -- The Multi-Valued Neuron -- MVN Learning -- Multilayer Feedforward Neural Network based on Multi-Valued Neurons (MLMVN) -- Multi-Valued Neuron with a Periodic Activation Function -- Applications of MVN and MLMVN. |
| 520 ## - RESUMEN, ETC. | |
| Nota de sumario, etc. | Complex-Valued Neural Networks have higher functionality, learn faster and generalize better than their real-valued counterparts. This book is devoted to the Multi-Valued Neuron (MVN) and MVN-based neural networks. It contains a comprehensive observation of MVN theory, its learning, and applications. MVN is a complex-valued neuron whose inputs and output are located on the unit circle. Its activation function is a function only of argument (phase) of the weighted sum. MVN derivative-free learning is based on the error-correction rule. A single MVN can learn those input/output mappings that are non-linearly separable in the real domain. Such classical non-linearly separable problems as XOR and Parity n are the simplest that can be learned by a single MVN. Another important advantage of MVN is a proper treatment of the phase information. These properties of MVN become even more remarkable when this neuron is used as a basic one in neural networks. The Multilayer Neural Network based on Multi-Valued Neurons (MLMVN) is an MVN-based feedforward neural network. Its backpropagation learning algorithm is derivative-free and based on the error-correction rule. It does not suffer from the local minima phenomenon. MLMVN outperforms many other machine learning techniques in terms of learning speed, network complexity and generalization capability when solving both benchmark and real-world classification and prediction problems. Another interesting application of MVN is its use as a basic neuron in multi-state associative memories. á The book is addressed to those readers who develop theoretical fundamentals of neural networks and use neural networks for solving various real-world problems. It should also be very suitable for Ph.D. and graduate students pursuing their degrees in computational intelligence. |
| 650 ## - ASIENTO SECUNDARIO DE MATERIA--TÉRMINO DE MATERIA | |
| Nombre de materia o nombre geográfico como elemento de entrada | Engineering. |
| 9 (RLIN) | 96 |
| 650 ## - ASIENTO SECUNDARIO DE MATERIA--TÉRMINO DE MATERIA | |
| Nombre de materia o nombre geográfico como elemento de entrada | Artificial intelligence. |
| 9 (RLIN) | 33648 |
| 650 ## - ASIENTO SECUNDARIO DE MATERIA--TÉRMINO DE MATERIA | |
| Nombre de materia o nombre geográfico como elemento de entrada | Engineering. |
| 9 (RLIN) | 96 |
| 650 ## - ASIENTO SECUNDARIO DE MATERIA--TÉRMINO DE MATERIA | |
| Nombre de materia o nombre geográfico como elemento de entrada | Artificial Intelligence (incl. Robotics). |
| 9 (RLIN) | 23200 |
| 650 ## - ASIENTO SECUNDARIO DE MATERIA--TÉRMINO DE MATERIA | |
| 9 (RLIN) | 33763 |
| Nombre de materia o nombre geográfico como elemento de entrada | COMPUTATIONAL INTELIGENCE |
| 710 ## - ENCABEZAMIENTO SECUNDARIO--NOMBRE CORPORATIVO | |
| Nombre corporativo o de jurisdicción como elemento de entrada | SpringerLink (Online service) |
| 9 (RLIN) | 111 |
| 856 ## - ACCESO ELECTRÓNICO | |
| Identificador uniforme del recurso URI | <a href="http://springer.escuelaing.metaproxy.org/book/10.1007/978-3-642-20353-4">http://springer.escuelaing.metaproxy.org/book/10.1007/978-3-642-20353-4</a> |
| Texto del enlace | ir a documento |
| Tipo de formato electrónico | URL |
| 942 ## - ELEMENTOS KOHA | |
| Fuente de clasificación o esquema de ordenación en estanterías | |
| Koha tipo de item | DOCUMENTOS DIGITALES |
| Disponibilidad | Mostrar en OPAC | Fuente de clasificación o esquema | Tipo de Descarte | Restricciones de uso | Estado | Código de colección | Localización permanente | Localización actual | Fecha adquisición | Proveedor | Forma de Adq | Precio normal de compra | Datos del ítem (Volumen, Tomo) | Préstamos totales | Signatura completa | Código de barras | Fecha última consulta | Número de ejemplar | Propiedades de Préstamo KOHA | Programa Académico |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Préstamo Normal | Digital | Biblioteca Jorge Álvarez Lleras | Biblioteca Jorge Álvarez Lleras | 2014-03-03 | Springer-444444025-OS1549 | Compra | 13770.00 | Ej. 1 | 006.3 223 | D000260 | 2014-10-14 | 1 | DOCUMENTOS DIGITALES | Biblioteca |
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