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Practical neural network recipes in C++ /
This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book pr...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boston :
Morgan Kaufmann,
[1993]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_ocn892067383 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 141003s1993 maua fob 000 0 eng d | ||
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| 020 | |a 9780080514338 |q (electronic bk.) | ||
| 020 | |a 0080514332 |q (electronic bk.) | ||
| 020 | |z 0124790402 | ||
| 020 | |z 9780124790407 | ||
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| 029 | 1 | |a NZ1 |b 15922268 | |
| 035 | |a (OCoLC)892067383 | ||
| 050 | 4 | |a QA76.87 |b M423 1993eb | |
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| 084 | |a DAT 717f |2 stub | ||
| 084 | |a DAT 358f |2 stub | ||
| 049 | |a UAMI | ||
| 100 | 1 | |a Masters, Timothy. | |
| 245 | 1 | 0 | |a Practical neural network recipes in C++ / |c Timothy Masters. |
| 264 | 1 | |a Boston : |b Morgan Kaufmann, |c [1993] | |
| 264 | 4 | |c ©1993 | |
| 300 | |a 1 online resource (xviii, 493 pages) : |b illustrations | ||
| 300 | |a 1 online resource (1 disc (3 1/2 in.)) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Accompanied by Diskette (702000135). | ||
| 504 | |a Includes bibliographical references (pages 479-490). | ||
| 520 | |a This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Front Cover; Practical Neural Network Recipes in C++; Copyright Page; Dedication; Table of Contents; Preface; Chapter 1. Foundations; Motivation; New Life for Old Techniques; Perceptrons and Linear Separability; Neural Network Capabilities; Basic Structure of a Neural Network; Training; Validation; Chapter 2. Classification; Binary Decisions; Multiple Classes; Supervised versus Unsupervised Training; Chapter 3. Autoassociation; Autoassociative Filtering; Noise Reduction; Learning a Prototype from Exemplars; Exposing Isolated Events; Pattern Completion; Error Correction; Data Compression. | |
| 505 | 8 | |a Chapter 4. Time-Series PredictionThe Basic Model; Input Data; Multiple Prediction; Multiple Predictors; Measuring Prediction Error; Chapter 5. Function Approximation; Univariate Function Approximation; Inverse Modeling; Multiple Regression; Chapter 6. Multilayer Feedforward Networks; Basic Architecture; Theoretical Discussion; Algorithms for Executing the Network; Training the Network; Training by Backpropagation of Errors; Training by Conjugate Gradients; Eluding Local Minima in Learning; When to Use a Multiple-Layer Feedforward Network; Chapter 7. Eluding Local Minima I: Simulated Annealing. | |
| 505 | 8 | |a OverviewChoosing the Annealing Parameters; Implementation in Feedforward Network Learning; A Sample Program; A Sample Function; Random Number Generation; Going on from Here; Chapter 8. Eluding Local Minima II: Genetic Optimization; Overview; Designing the Genetic Structure; Evaluation; Parent Selection; Reproduction; Mutation; A Genetic Minimization Subroutine; Some Functions for Genetic Optimization; Advanced Topics in Genetic Optimization; Chapter 9. Regression and Neural Networks; Overview; Singular-Value Decomposition; Regression in Neural Networks. | |
| 505 | 8 | |a Chapter 10. Designing Feedforward Network ArchitecturesHow Many Hidden Layers?; How Many Hidden Neurons?; How Long Do I Train This Thing???; Chapter 11. Interpreting Weights: How Does This Thing Work?; Features Used by Networks in General; Features Used by a Particular Network; Chapter 12. Probabilistic Neural Networks; Overview; Computational Aspects; Optimizing Sigma; A Sample Program; Bayesian Confidence Measures; Autoassociative Versions; When to Use a Probabilistic Neural Network; Chapter 13. Functional Link Networks; Application to Nonlinear Approximation. | |
| 505 | 8 | |a Mathematics of the Functional Link NetworkWhen to Use a Functional Link Network; Chapter 14. Hybrid Networks; Functional Link Net as a Hidden Layer; Fast Bayesian Confidences; Attention-based Processing; Factorable Problems; Chapter 15. Designing the Training Set; Number of Samples; Borderline Cases; Hidden Bias; Balancing the Classes; Fudging Cases; Chapter 16. Preparing Input Data; General Considerations; Types of Measurements; Is Scaling Always Necessary?; Transformations; Circular Discontinuity; Outliers; Missing Data; Chapter 17. Fuzzy Data and Processing. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a C++ (Computer program language) | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a C++ (Langage de programmation) | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a C++ (Computer program language) |2 fast |0 (OCoLC)fst00843286 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 776 | 0 | 8 | |i Print version: |a Masters, Timothy. |t Practical neural network recipes in C++ |z 0124790402 |w (OCoLC)762079168 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9780080514338/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a EBSCOhost |b EBSC |n 919356 | ||
| 994 | |a 92 |b IZTAP | ||