Neural networks : (Registro nro. 12987)
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| 000 -Cabecera | |
|---|---|
| Campo de control de longitud fija | 07165nam a2200265 a 4500 |
| 003 - Identificador del Número de control | |
| Identificador del número de control | AR-sfUTN |
| 008 - Códigos de información de longitud fija-Información general | |
| Códigos de información de longitud fija | 170717s1999 ||||| |||| 00| 0 eng d |
| 020 ## - ISBN | |
| ISBN | 0132733501 |
| 080 ## - CDU | |
| Clasificación Decimal Universal | 004.85 H331 |
| Edición de la CDU | 2000 |
| 100 1# - Punto de acceso principal-Nombre de persona | |
| Nombre personal | Haykin, Simon |
| 245 10 - Mención de título | |
| Título | Neural networks : |
| Resto del título | a comprehensive foundation / |
| Mención de responsabilidad | Simon Haykin. |
| 250 ## - Mención de edición | |
| Mención de edición | 2nd |
| 260 ## - Publicación, distribución, etc. (pie de imprenta) | |
| Lugar de publicación, distribución, etc. | Upper Saddle River, New Jersey : |
| Nombre del editor, distribuidor, etc. | Prentice-Hall, |
| Fecha de publicación, distribución, etc. | 1999 |
| 300 ## - Descripción física | |
| Extensión | 842 p. |
| 336 ## - Tipo de contenido | |
| Fuente | rdacontent |
| Término de tipo de contenido | texto |
| Código de tipo de contenido | txt |
| 337 ## - Tipo de medio | |
| Fuente | rdamedia |
| Nombre del tipo de medio | sin mediación |
| Código del tipo de medio | n |
| 338 ## - Tipo de soporte | |
| Fuente | rdacarrier |
| Nombre del tipo de soporte | volumen |
| Código del tipo de soporte | nc |
| 505 80 - Nota de contenido con formato | |
| Nota de contenido con formato | CONTENIDO<br/>1. Introduction 1<br/>What Is a Neural Network? 1<br/>Human Brain 6<br/>Models of a Neuron 10<br/>Neural Networks Viewed as Directed Graphs 15<br/>Feedback 18<br/>Network Architectures 21<br/>Knowledge Representation 23<br/>Artificial Intelligence and Neural Networks 34<br/>Historical Notes 38<br/>2. Learning Processes 50<br/>Error-Correction Learning 51<br/>Memory-Based Learning 53<br/>Hebbian Learning 55<br/>Competitive Learning 58<br/>Boltzmann Learning 60<br/>Credit Assignment Problem 62<br/>Learning with a Teacher 63<br/>Learning without a Teacher 64<br/>Learning Tasks 66<br/>Memory 75<br/>Adaptation 83<br/>Statistical Nature of the Learning Process 84<br/>Statistical Learning Theory 89<br/>Probably Approximately Correct Model of Learning 102<br/>3. Single Layer Perceptrons 117<br/>Adaptive Filtering Problem 118<br/>Unconstrained Optimization Techniques 121<br/>Linear Least-Squares Filters 126<br/>Least-Mean-Square Algorithm 128<br/>Learning Curves 133<br/>Learning Rate Annealing Techniques 134<br/>Perceptron 135<br/>Perceptron Convergence Theorem 137<br/>Relation Between the Perceptron and Bayes Classifier for a Gaussian Environment 143<br/>4. Multilayer Perceptrons 156<br/>Some Preliminaries 159<br/>Back-Propagation Algorithm 161<br/>Summary of the Back-Propagation Algorithm 173<br/>XOR Problem 175<br/>Heuristics for Making the Back-Propagation Algorithm Perform Better 178<br/>Output Representation and Decision Rule 184<br/>Computer Experiment 187<br/>Feature Detection 199<br/>Back-Propagation and Differentiation 202<br/>Hessian Matrix 204<br/>Generalization 205<br/>Approximations of Functions 208<br/>Cross-Validation 213<br/>Network Pruning Techniques 218<br/>Virtues and Limitations of Back-Propagation Learning 226<br/>Accelerated Convergence of Back-Propagation Learning 233<br/>Supervised Learning Viewed as an Optimization Problem 234<br/>Convolutional Networks 245<br/>5. Radial-Basis Function Networks 256<br/>Cover's Theorem on the Separability of Patterns 257<br/>Interpolation Problem 262<br/>Supervised Learning as an Ill-Posed Hypersurface Reconstruction Problem 265<br/>Regularization Theory 267<br/>Regularization Networks 277<br/>Generalized Radial-Basis Function Networks 278<br/>XOR Problem (Revisited) 282<br/>Estimation of the Regularization Parameter 284<br/>Approximation Properties of RBF Networks 290<br/>Comparison of RBF Networks and Multilayer Perceptrons 293<br/>Kernel Regression and Its Its Relation to RBF Networks 294<br/>Learning Strategies 298<br/>Computer Experiment 305<br/>6. Support Vector Machines 318<br/>Optimal Hyperplane for Linearly Separable Patterns 319<br/>Optimal Hyperplane for Nonseparable Patterns 326<br/>How to Build a Support Vector Machine for Pattern Recognition 329<br/>Example: XOR Problem (Revisited) 335<br/>Computer Experiment 337<br/>epsis-Insensitive Loss Function 339<br/>Support Vector Machines for Nonlinear Regression 340<br/>7. Committee Machines 351<br/>Ensemble Averaging 353<br/>Computer Experiment I 355<br/>Boosting 357<br/>Computer Experiment II 364<br/>Associative Gaussian Mixture Model 366<br/>Hierarchical Mixture of Experts Model 372<br/>Model Selection Using a Standard Decision Tree 374<br/>A Priori and A Posteriori Probabilities 377<br/>Maximum Likelihood Estimation 378<br/>Learning Strategies for the HME Model 380<br/>EM Algorithm 382<br/>Application of the EM Algorithm to the HME Model 383<br/>8. Principal Components Analysis 392<br/>Some Intuitive Principles of Self-Organization 393<br/>Principal Components Analysis 396<br/>Hebbian-Based Maximum Eigenfilter 404<br/>Hebbian-Based Principal Components Analysis 413<br/>Computer Experiment: Image Coding 419<br/>Adaptive Principal Components Analysis Using Lateral Inhibition 422<br/>Two Classes of PCA Algorithms 430<br/>Batch and Adaptive Methods of Computation 430<br/>Kernel-Based Principal Components Analysis 432<br/>9. Self-Organizing Maps 443<br/>Two Basic Feature-Mapping Models 444<br/>Self-Organizing Map 446<br/>Summary of the SOM Algorithm 453<br/>Properties of the Feature Map 454<br/>Computer Simulations 461<br/>Learning Vector Quantization 466<br/>Computer Experiment: Adaptive Pattern Classification 468<br/>Hierarchical Vector Quantization 470<br/>Contextual Maps 474<br/>10. Information-Theoretic Models 484<br/>Entropy 485<br/>Maximum Entropy Principle 490<br/>Mutual Information 492<br/>Kullback-Leibler Divergence 495<br/>Mutual Information as an Objective Function To Be Optimized 498<br/>Maximum Mutual Information Principle 499<br/>Infomax and Redundancy Reduction 503<br/>Spatially Coherent Features 506<br/>Spatially Incoherent Features 508<br/>Independent Components Analysis 510<br/>Computer Experiment 523<br/>Maximum Likelihood Estimation 525<br/>Maximum Entropy Method 529<br/>11. Stochastic Machines And Their Approximates Rooted in Statistical Mechanics 545<br/>Statistical Mechanics 546<br/>Markov Chains 548<br/>Metropolis Algorithm 556<br/>Simulated Annealing 558<br/>Gibbs Sampling 561<br/>Boltzmann Machine 562<br/>Sigmoid Belief Networks 569<br/>Helmholtz Machine 574<br/>Mean-Field Theory 576<br/>Deterministic Boltzmann Machine 578<br/>Deterministic Sigmoid Belief Networks 579<br/>Deterministic Annealing 586<br/>12. Neurodynamic Programming 603<br/>Markovian Decision Processes 604<br/>Bellman's Optimality Criterion 607<br/>Policy Iteration 610<br/>Value Iteration 612<br/>Neurodynamic Programming 617<br/>Approximate Policy Iteration 618<br/>Q-Learning 622<br/>Computer Experiment 627<br/>13. Temporal Processing Using Feedforward Networks 635<br/>Short-term Memory Structures 636<br/>Network Architectures for Temporal Processing 640<br/>Focused Time Lagged Feedforward Networks 643<br/>Computer Experiment 645<br/>Universal Myopic Mapping Theorem 646<br/>Spatio-Temporal Models of a Neuron 648<br/>Distributed Time Lagged Feedforward Networks 651<br/>Temporal Back-Propagation Algorithm 652<br/>14. Neurodynamics 664<br/>Dynamical Systems 666<br/>Stability of Equilibrium States 669<br/>Attractors 674<br/>Neurodynamical Models 676<br/>Manipulation of Attractors as a Recurrent Network Paradigm 680<br/>Hopfield Models 680<br/>Computer Experiment I 696<br/>Cohen-Grossberg Theorem 701<br/>Brain-State-in-a-Box Model 703<br/>Computer Experiment II 709<br/>Strange Attractors and Chaos 709<br/>Dynamic Reconstruction of a Chaotic Process 714<br/>Computer Experiment III 718<br/>15. Dynamically Driven Recurrent Networks 732<br/>Recurrent Network Architectures 733<br/>State-Space Model 739<br/>Nonlinear Autoregressive with Exogenous Inputs Model 746<br/>Computation Power of Recurrent Networks 747<br/>Learning Algorithms 750<br/>Back-Propagation Through Time 751<br/>Real-Time Recurrent Learning 756<br/>Kalman Filters 762<br/>Decoupled Extended Kalman Filters 765<br/>Computer Experiment 770<br/>Vanishing Gradients in Recurrent Networks 773<br/>System Identification 776<br/>Model-Reference Adaptive Control 780<br/>Epilogue 790<br/>Bibliography 796<br/>Index 837<br/> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Tipo de ítem Koha | Libro |
| Esquema de clasificación | Clasificación Decinal Universal |
| 952 ## - Información del item y localización (Koha) | |
| Estado | |
| Estado de conservación | |
| Tipo de préstamo | Préstamo |
| Biblioteca | Facultad Regional Santa Fe - Biblioteca "Rector Comodoro Ing. Jorge Omar Conca" |
| -- | Facultad Regional Santa Fe - Biblioteca "Rector Comodoro Ing. Jorge Omar Conca" |
| Fecha de adquisición | 2018-02-02 |
| Origen de la adquisición | Compra Exp. 23/2010 |
| Número de inventario | 10439 |
| ST completa de Koha | 004.85 H331 |
| Código de barras | 10439 |
| Precio efectivo a partir de | 2018-02-02 |
| Tipo de ítem Koha | Libro |
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