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Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson
| Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson |
| Autore | Garson G. David |
| Pubbl/distr/stampa | London, : Sage Publications, 1998 |
| Descrizione fisica | 1 online resource (201 p.) |
| Disciplina | 006.3/2 |
| Collana | New technologies for social research |
| Soggetto topico |
Neural networks (Computer science)
Social sciences - Mathematical models Social sciences - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-282-62312-5
9786612623127 0-85702-627-5 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover; Table of Contents; 1 - Introduction to Neural Network Analysis; 2 - The Terminology of Neural Network Analysis; 3 - The Backpropogation Model; 4 - Alternative Network Paradigms; 5 - Methodological Considerations; 6 - Neural Network Software; 7 - Example: Analysing Census Data with Neural Connection; 8 - Conclusion; Notes; References; Index |
| Record Nr. | UNINA-9910458260503321 |
Garson G. David
|
||
| London, : Sage Publications, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson
| Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson |
| Autore | Garson G. David |
| Pubbl/distr/stampa | London, : Sage Publications, 1998 |
| Descrizione fisica | 1 online resource (201 p.) |
| Disciplina | 006.3/2 |
| Collana | New technologies for social research |
| Soggetto topico |
Neural networks (Computer science)
Social sciences - Mathematical models Social sciences - Data processing |
| ISBN |
1-282-62312-5
9786612623127 0-85702-627-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover; Table of Contents; 1 - Introduction to Neural Network Analysis; 2 - The Terminology of Neural Network Analysis; 3 - The Backpropogation Model; 4 - Alternative Network Paradigms; 5 - Methodological Considerations; 6 - Neural Network Software; 7 - Example: Analysing Census Data with Neural Connection; 8 - Conclusion; Notes; References; Index |
| Record Nr. | UNINA-9910791379403321 |
|
Garson G. David
|
||
| London, : Sage Publications, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson
| Neural networks [[electronic resource] ] : an introductory guide for social scientists / / G. David Garson |
| Autore | Garson G. David |
| Pubbl/distr/stampa | London, : Sage Publications, 1998 |
| Descrizione fisica | 1 online resource (201 p.) |
| Disciplina | 006.3/2 |
| Collana | New technologies for social research |
| Soggetto topico |
Neural networks (Computer science)
Social sciences - Mathematical models Social sciences - Data processing |
| ISBN |
1-282-62312-5
9786612623127 0-85702-627-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover; Table of Contents; 1 - Introduction to Neural Network Analysis; 2 - The Terminology of Neural Network Analysis; 3 - The Backpropogation Model; 4 - Alternative Network Paradigms; 5 - Methodological Considerations; 6 - Neural Network Software; 7 - Example: Analysing Census Data with Neural Connection; 8 - Conclusion; Notes; References; Index |
| Record Nr. | UNINA-9910815927903321 |
|
Garson G. David
|
||
| London, : Sage Publications, 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich
| Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich |
| Autore | Zaknich Anthony |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, c2003 |
| Descrizione fisica | 1 online resource (510 p.) |
| Disciplina | 006.3/2 |
| Collana | Series on innovative intelligence |
| Soggetto topico |
Neural networks (Computer science)
Signal processing - Digital techniques Intelligent control systems |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-94787-3
9786611947873 981-279-685-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Acknowledgments; Foreword; Preface; 1. Introduction; 1.1 Motivation for ANNs; 1.2 ANN Definitions and Main Types; 1.3 Specific ANN Models; 1.4 ANN Black Box Model; 1.5 ANN Implementation; 1.6 When To Use an ANN; 1.7 How To Use an ANN
1.8 General Applications 1.9 Pattern Recognition Examples; 1.9.1 Sheep Eating Phase Identification from Jaw Sounds; 1.9.2 Particle Isolation in SEM Images; 1.9.3 Oxalate Needle Detection in Microscope Images ; 1.10 Function Mapping and Filtering Examples 1.10.1 Water Level from Resonant Sound Analysis 1.10.2 Nonlinear Signal Filtering; 1.11 Motor Control Example; 1.12 ANN Summary; References; 2. A Brief Historical Overview; 2.1 ANN History to 1970; 2.1.1 Key Events prior to 1970; 2.2 ANN History after 1970 2.2.1 Key Events after 1970 to the Mid 1980's 2.2.2 Developments after the Mid 1980's; 2.2.3 Nonparametric Learning From Finite Data; 2.3 Reasons for the Resurgence of Interest in ANNs; 2.4 Historical Summary ; References; 3. Basic Concepts; 3.1 The Basic Model of the Neuron 3.2 Activation Functions 3.3 Topologies; 3.4 Learning; 3.4.1 A Basic Supervised Learning Algorithm; 3.4.2 A Basic Unsupervised Learning Algorithm; 3.5 The Basic McCulloch Pitts and Perceptron Models; 3.6 Vectors Spaces and Matrix Models; 3.6.1 ANN Classifiers 3.6.2 Vectors and Feature Spaces |
| Record Nr. | UNINA-9910454089903321 |
|
Zaknich Anthony
|
||
| River Edge, NJ, : World Scientific, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich
| Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich |
| Autore | Zaknich Anthony |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, c2003 |
| Descrizione fisica | 1 online resource (510 p.) |
| Disciplina | 006.3/2 |
| Collana | Series on innovative intelligence |
| Soggetto topico |
Neural networks (Computer science)
Signal processing - Digital techniques Intelligent control systems |
| ISBN |
1-281-94787-3
9786611947873 981-279-685-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Acknowledgments; Foreword; Preface; 1. Introduction; 1.1 Motivation for ANNs; 1.2 ANN Definitions and Main Types; 1.3 Specific ANN Models; 1.4 ANN Black Box Model; 1.5 ANN Implementation; 1.6 When To Use an ANN; 1.7 How To Use an ANN
1.8 General Applications 1.9 Pattern Recognition Examples; 1.9.1 Sheep Eating Phase Identification from Jaw Sounds; 1.9.2 Particle Isolation in SEM Images; 1.9.3 Oxalate Needle Detection in Microscope Images ; 1.10 Function Mapping and Filtering Examples 1.10.1 Water Level from Resonant Sound Analysis 1.10.2 Nonlinear Signal Filtering; 1.11 Motor Control Example; 1.12 ANN Summary; References; 2. A Brief Historical Overview; 2.1 ANN History to 1970; 2.1.1 Key Events prior to 1970; 2.2 ANN History after 1970 2.2.1 Key Events after 1970 to the Mid 1980's 2.2.2 Developments after the Mid 1980's; 2.2.3 Nonparametric Learning From Finite Data; 2.3 Reasons for the Resurgence of Interest in ANNs; 2.4 Historical Summary ; References; 3. Basic Concepts; 3.1 The Basic Model of the Neuron 3.2 Activation Functions 3.3 Topologies; 3.4 Learning; 3.4.1 A Basic Supervised Learning Algorithm; 3.4.2 A Basic Unsupervised Learning Algorithm; 3.5 The Basic McCulloch Pitts and Perceptron Models; 3.6 Vectors Spaces and Matrix Models; 3.6.1 ANN Classifiers 3.6.2 Vectors and Feature Spaces |
| Record Nr. | UNINA-9910782282403321 |
|
Zaknich Anthony
|
||
| River Edge, NJ, : World Scientific, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich
| Neural networks for intelligent signal processing [[electronic resource] /] / Anthony Zaknich |
| Autore | Zaknich Anthony |
| Pubbl/distr/stampa | River Edge, NJ, : World Scientific, c2003 |
| Descrizione fisica | 1 online resource (510 p.) |
| Disciplina | 006.3/2 |
| Collana | Series on innovative intelligence |
| Soggetto topico |
Neural networks (Computer science)
Signal processing - Digital techniques Intelligent control systems |
| ISBN |
1-281-94787-3
9786611947873 981-279-685-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Contents; Acknowledgments; Foreword; Preface; 1. Introduction; 1.1 Motivation for ANNs; 1.2 ANN Definitions and Main Types; 1.3 Specific ANN Models; 1.4 ANN Black Box Model; 1.5 ANN Implementation; 1.6 When To Use an ANN; 1.7 How To Use an ANN
1.8 General Applications 1.9 Pattern Recognition Examples; 1.9.1 Sheep Eating Phase Identification from Jaw Sounds; 1.9.2 Particle Isolation in SEM Images; 1.9.3 Oxalate Needle Detection in Microscope Images ; 1.10 Function Mapping and Filtering Examples 1.10.1 Water Level from Resonant Sound Analysis 1.10.2 Nonlinear Signal Filtering; 1.11 Motor Control Example; 1.12 ANN Summary; References; 2. A Brief Historical Overview; 2.1 ANN History to 1970; 2.1.1 Key Events prior to 1970; 2.2 ANN History after 1970 2.2.1 Key Events after 1970 to the Mid 1980's 2.2.2 Developments after the Mid 1980's; 2.2.3 Nonparametric Learning From Finite Data; 2.3 Reasons for the Resurgence of Interest in ANNs; 2.4 Historical Summary ; References; 3. Basic Concepts; 3.1 The Basic Model of the Neuron 3.2 Activation Functions 3.3 Topologies; 3.4 Learning; 3.4.1 A Basic Supervised Learning Algorithm; 3.4.2 A Basic Unsupervised Learning Algorithm; 3.5 The Basic McCulloch Pitts and Perceptron Models; 3.6 Vectors Spaces and Matrix Models; 3.6.1 ANN Classifiers 3.6.2 Vectors and Feature Spaces |
| Record Nr. | UNINA-9910809089503321 |
|
Zaknich Anthony
|
||
| River Edge, NJ, : World Scientific, c2003 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks with Model Compression [[electronic resource] /] / by Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann
| Neural Networks with Model Compression [[electronic resource] /] / by Baochang Zhang, Tiancheng Wang, Sheng Xu, David Doermann |
| Autore | Zhang Baochang |
| Edizione | [1st ed. 2024.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
| Descrizione fisica | 1 online resource (267 pages) |
| Disciplina | 006.3/2 |
| Altri autori (Persone) |
WangTiancheng
XuSheng DoermannDavid |
| Collana | Computational Intelligence Methods and Applications |
| Soggetto topico |
Machine learning
Artificial intelligence Image processing - Digital techniques Computer vision Machine Learning Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics Computer Vision |
| ISBN | 981-9950-68-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction -- Chapter 2. Binary Neural Networks -- Chapter 3. Binary Neural Architecture Search -- Chapter 4. Quantization of Neural Networks -- Chapter 5. Network Pruning -- Chapter 6. Applications. |
| Record Nr. | UNINA-9910831008703321 |
|
Zhang Baochang
|
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| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller
| Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller |
| Edizione | [1st ed. 1998.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 |
| Descrizione fisica | 1 online resource (VIII, 432 p.) |
| Disciplina | 006.3/2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computers
Artificial intelligence Microprocessors Pattern recognition Computational complexity Computation by Abstract Devices Artificial Intelligence Processor Architectures Pattern Recognition Complexity |
| ISBN | 3-540-49430-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Speeding Learning -- Efficient BackProp -- Regularization Techniques to Improve Generalization -- Early Stopping - But When? -- A Simple Trick for Estimating the Weight Decay Parameter -- Controlling the hyperparameter search in MacKay’s Bayesian neural network framework -- Adaptive Regularization in Neural Network Modeling -- Large Ensemble Averaging -- Improving Network Models and Algorithmic Tricks -- Square Unit Augmented Radially Extended Multilayer Perceptrons -- A Dozen Tricks with Multitask Learning -- Solving the Ill-Conditioning in Neural Network Learning -- Centering Neural Network Gradient Factors -- Avoiding roundoff error in backpropagating derivatives -- Representing and Incorporating Prior Knowledge in Neural Network Training -- Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation -- Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton -- Neural Network Classification and Prior Class Probabilities -- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Tricks for Time Series -- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- How to Train Neural Networks. |
| Record Nr. | UNINA-9910143467403321 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller
| Neural Networks: Tricks of the Trade [[electronic resource] /] / edited by Genevieve B. Orr, Klaus-Robert Müller |
| Edizione | [1st ed. 1998.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 |
| Descrizione fisica | 1 online resource (VIII, 432 p.) |
| Disciplina | 006.3/2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Computers
Artificial intelligence Microprocessors Pattern recognition Computational complexity Computation by Abstract Devices Artificial Intelligence Processor Architectures Pattern Recognition Complexity |
| ISBN | 3-540-49430-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Speeding Learning -- Efficient BackProp -- Regularization Techniques to Improve Generalization -- Early Stopping - But When? -- A Simple Trick for Estimating the Weight Decay Parameter -- Controlling the hyperparameter search in MacKay’s Bayesian neural network framework -- Adaptive Regularization in Neural Network Modeling -- Large Ensemble Averaging -- Improving Network Models and Algorithmic Tricks -- Square Unit Augmented Radially Extended Multilayer Perceptrons -- A Dozen Tricks with Multitask Learning -- Solving the Ill-Conditioning in Neural Network Learning -- Centering Neural Network Gradient Factors -- Avoiding roundoff error in backpropagating derivatives -- Representing and Incorporating Prior Knowledge in Neural Network Training -- Transformation Invariance in Pattern Recognition — Tangent Distance and Tangent Propagation -- Combining Neural Networks and Context-Driven Search for Online, Printed Handwriting Recognition in the Newton -- Neural Network Classification and Prior Class Probabilities -- Applying Divide and Conquer to Large Scale Pattern Recognition Tasks -- Tricks for Time Series -- Forecasting the Economy with Neural Nets: A Survey of Challenges and Solutions -- How to Train Neural Networks. |
| Record Nr. | UNISA-996465914003316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Neural-based orthogonal data fitting : the EXIN neural networks / / Giansalvo Cirrincione, Maurizio Cirrincione
| Neural-based orthogonal data fitting : the EXIN neural networks / / Giansalvo Cirrincione, Maurizio Cirrincione |
| Autore | Cirrincione Giansalvo <1959-> |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , c2010 |
| Descrizione fisica | 1 online resource (278 p.) |
| Disciplina |
006.3/2
621.399 |
| Altri autori (Persone) | CirrincioneMaurizio <1961-> |
| Collana | Adaptive and learning systems for signal processing, communications and control series |
| Soggetto topico |
Neural networks (Computer science)
Numerical analysis Orthogonalization methods |
| ISBN |
1-118-09774-2
1-283-10079-7 9786613100795 0-470-63828-1 0-470-63827-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Foreword -- Preface -- 1 The Total Least Squares Problems -- 1.1 Introduction -- 1.2 Some TLS Applications -- 1.3 Preliminaries -- 1.4 Ordinary Least Squares Problems -- 1.5 Basic TLS Problem -- 1.6 Multidimensional TLS Problem -- 1.7 Nongeneric Unidimensional TLS Problem -- 1.8 Mixed OLS-TLS Problem -- 1.9 Algebraic Comparisons Between TLS and OLS -- 1.10 Statistical Properties and Validity -- 1.11 Basic Data Least Squares Problem -- 1.12 The Partial TLS Algorithm -- 1.13 Iterative Computation Methods -- 1.14 Rayleigh Quotient Minimization Non Neural and Neural Methods -- 2 The MCA EXIN Neuron -- 2.1 The Rayleigh Quotient -- 2.2 The Minor Component Analysis -- 2.3 The MCA EXIN Linear Neuron -- 2.4 The Rayleigh Quotient Gradient Flows -- 2.5 The MCA EXIN ODE Stability Analysis -- 2.6 Dynamics of the MCA Neurons -- 2.7 Fluctuations (Dynamic Stability) and Learning Rate -- 2.8 Numerical Considerations -- 2.9 TLS Hyperplane Fitting -- 2.10 Simulations for the MCA EXIN Neuron -- 2.11 Conclusions -- 3 Variants of the MCA EXIN Neuron -- 3.1 High-Order MCA Neurons -- 3.2 The Robust MCA EXIN Nonlinear Neuron (NMCA EXIN) -- 3.3 Extensions of the Neural MCA -- 4 Introduction to the TLS EXIN Neuron -- 4.1 From MCA EXIN to TLS EXIN -- 4.2 Deterministic Proof and Batch Mode -- 4.3 Acceleration Techniques -- 4.4 Comparison with TLS GAO -- 4.5 A TLS Application: Adaptive IIR Filtering -- 4.6 Numerical Considerations -- 4.7 The TLS Cost Landscape: Geometric Approach -- 4.8 First Considerations on the TLS Stability Analysis -- 5 Generalization of Linear Regression Problems -- 5.1 Introduction -- 5.2 The Generalized Total Least Squares (GeTLS EXIN) Approach -- 5.3 The GeTLS Stability Analysis -- 5.4 Neural Nongeneric Unidimensional TLS -- 5.5 Scheduling -- 5.6 The Accelerated MCA EXIN Neuron (MCA EXIN+) -- 5.7 Further Considerations -- 5.8 Simulations for the GeTLS EXIN Neuron -- 6 The GeMCA EXIN Theory -- 6.1 The GeMCA Approach -- 6.2 Analysis of Matrix K -- 6.3 Analysis of the Derivative of the Eigensystem of GeTLS EXIN.
6.4 Rank One Analysis Around the TLS Solution -- 6.5 The GeMCA Spectra -- 6.6 Qualitative Analysis of the Critical Points of the GeMCA EXIN Error Function -- 6.7 Conclusion -- References -- Index. |
| Record Nr. | UNINA-9910139216203321 |
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Cirrincione Giansalvo <1959->
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| Hoboken, New Jersey : , : Wiley, , c2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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