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| From Data to Models: Complexity and Challenges in Understanding Biological, Ecological, and Natural Systems | |
| Introduction | |
| Layout of the Book | |
| Fundamentals of Neural Networks and Models for Linear Data Analysis | |
| Introduction and Overview | |
| Neural Networks and Their Capabilities | |
| Inspirations from Biology | |
| Modeling Information Processing in Neurons | |
| Neuron Models and Learning Strategies | |
| Models for Prediction and Classification | |
| Practical Examples of Linear Neuron Models on Real Data | |
| Comparison with Linear Statistical Methods | |
| Summary | |
| Problems | |
| Neural Networks for Nonlinear Pattern Recognition | |
| Overview and Introduction | |
| Nonlinear Neurons | |
| Practical Example of Modeling with Nonlinear Neurons | |
| Comparison with Nonlinear Regression | |
| One-Input Multilayer Nonlinear Networks | |
| Two-Input Multilayer Perceptron Network | |
| Case Studies on Nonlinear Classification and Prediction with Nonlinear Networks | |
| Multidimensional Data Modeling with Nonlinear Multilayer Perceptron Networks | |
| Summary | |
| Problems | |
| Learning of Nonlinear Patterns by Neural Networks | |
| Introduction and Overview | |
| Supervised Training of Networks for Nonlinear Pattern Recognition | |
| Gradient Descent and Error Minimization | |
| Backpropagation Learning and Illustration with an Example and Case Study | |
| Delta-Bar-Delta Learning and Illustration with an Example and Case Study | |
| Steepest Descent Method Presented with an Example | |
| Comparison of First Order Learning Methods | |
| Second-Order Methods of Error Minimization and Weight Optimization | |
| Comparison of First Order and Second Order Learning Methods Illustrated through an Example | |
| Summary | |
| Problems | |
| Implementation of Neural Network Models for Extracting Reliable Patterns From Data | |
| Introduction and Overview | |
| Bias-Variance Tradeoff | |
| Illustration of Early Stopping and Regularization | |
| Improving Generalization of Neural Networks | |
| Network structure Optimization and Illustration with Examples | |
| Reducing Structural Complexity of Networks by Pruning | |
| Demonstration of Pruning with Examples | |
| Robustness of a Network to Perturbation of Weights Illustrated Using an Example | |
| Summary | |
| Problems | |
| Data Exploration, Dimensionality Reduction, and Feature Extraction | |
| Introduction and Overview | |
| Data Visualization Presented on Example Data | |
| Correlation and Covariance between Variables | |
| Normalization of Data | |
| Example Illustrating Correlation, Covariance and Normalization | |
| Selecting Relevant Inputs | |
| Dimensionality Reduction and Feature Extraction | |
| Example Illustrating Input Selection and Feature Extraction | |
| Outlier Detection | |
| Noise | |
| Case Study: Illustrating Input Selection and Dimensionality Reduction for a | |
| Practical Problem | |
| Summary | |
| Problems | |
| Assessment of Uncertainty of Neural Network Models Using Bayesian Statistics | |
| Introduction and Overview | |
| Estimating Weight Uncertainty Using Bayesian Statistics | |
| Case study Illustrating Weight Probability Distribution | |
| Assessing Uncertainty of Neural Network Outputs Using Bayesian Statistics | |
| Case Study Illustrating Uncertainty Assessment of Output Errors | |
| Assessing the Sensitivity of Network Outputs to Inputs | |
| Case Study Illustrating Uncertainty Assessment of Network Sensitivity to Inputs | |
| Summary | |
| Problems | |
| Discovering Unknown Clusters in Data With Self-Organizing Maps | |
| Introduction and Overview | |
| Structure of Unsupervised Networks for Clustering Multidimensional Data | |
| Learning in Unsupervised Networks | |
| Implementation of Competitive Learning Illustrated Through Examples | |
| Self-Organizing Feature Maps | |
| Examples and Case Studies Using Self-Organizing Maps on Multi-Dimensional Data | |
| Map Quality and Features Presented through Examples | |
| Illustration of Forming Clusters on the Map and Cluster Characteristics | |
| Map Validation and an Example | |
| Evolving Self-Organizing Maps | |
| Examples Illustrating Various Evolving Self Organizing Maps | |
| Summary | |
| Problems | |
| Neural Networks for Time-Series Forecasting | |
| Introduction and Overview | |
| Linear Forecasting of Time-Series with Statistical and Neural Network Models | |
| Example Case Study | |
| Neural Networks for Nonlinear Time-Series Forecasting | |
| Example Case Study | |
| Hybrid Linear (ARIMA) and Nonlinear Neural Network Models | |
| Example Case Study | |
| Automatic Generation of Network Structure Using Simplest Structure Concept-Illustrated Through Practical Application Case Study | |
| Generalized Neuron Network and Illustration Through Practical Application Case | |
| Study | |
| Dynamically Driven Recurrent Networks | |
| Practical Application Case Studies | |
| Bias and Variance in Time-Series Forecasting Illustrated Through an Example | |
| Long-Term Forecasting and a Case study | |
| Input Selection for Time-Series Forecasting | |
| Case study for Input Selection | |
| Summary | |
| Problems | |
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