Inhaltsangabe
1 Adaptive Technical Analysis in the Financial Markets Using Machine Learning: a Statistical View.- 1.1 'Technical Analysis' in Finance: a Brief Background.- 1.2 The 'Moving Windows' Paradigm.- 1.3 Post-Hoc Performance Assessment.- 1.3.1 The Effect of Dividends.- 1.3.2 Transaction Costs Approximations.- 1.4 Genetic programming.- 1.5 Support-Vector Machines.- 1.6 Neural Networks.- 1.7 Discussion.- References.- 2 Higher Order Neural Networks for Satellite Weather Prediction.- 2.1 Introduction.- 2.2 Higher Order Neural Networks.- 2.2.1 Polynomial Higher-Order Neural Networks.- 2.2.2 Trigonometric Higher-Order Neural Networks.- Output Neurons in THONN Model#1.- Second Hidden Layer Neurons in THONN Model#1.- First Hidden Layer Neurons in THONN Model#1.- 2.2.3 Neuron-Adaptive Higher-Order Neural Network.- 2.3 Artificial Neural Network Groups.- 2.3.1 ANN Groups.- 2.3.2 PHONN, THONN & NAHONN Groups.- 2.4 Weather Forecasting & ANNs.- 2.5 HONN Models for Half-hour Rainfall Prediction.- 2.5.1 PT-HONN Model.- 2.5.2 A-PHONN Model.- 2.5.3 M-PHONN Model.- 2.5.4 Satellite Rainfall Estimation Results.- 2.6 ANSER System for Rainfall Estimation.- 2.6.1 ANSER Architecture.- 2.6.2 ANSER Operation.- 2.6.3 Reasoning Network Based on ANN Groups.- 2.6.4 Rainfall Estimation Results.- 2.7 Summary.- Acknowledgements.- References.- Appendix-A Second Hidden Layer (multiply) Neurons.- Appendix-B First Hidden Layer Neurons.- 3 Independent Component Analysis.- 3.1 Introduction.- 3.2 Independent Component Analysis Methods.- 3.2.1 Basic Principles and Background.- 3.2.2 Mutual Information Methods.- 3.2.3 InfoMax ICA Algorithm.- 3.2.4 Natural/Relative Gradient Methods.- 3.2.5 Extended InfoMax.- 3.2.6 Adaptive Mutual Information.- 3.2.7 Fixed Point ICA Algorithm.- 3.2.8 Decorrelation and Rotation Methods.- 3.2.9 Comon Decorrelation and Rotation Algorithm.- 3.2.10 Temporal Decorrelation Methods.- 3.2.11 Molgedey and Schuster Temporal Correlation Algorithm.- 3.2.12 Spatio-temporal ICA Methods.- 3.2.13 Cumulant Tensor Methods.- 3.2.14 Nonlinear Decorrelation Methods.- 3.3 Applications of ICA.- 3.3.1 Guidelines for Applications of ICA.- 3.3.2 Biomedical Signal Processing.- 3.3.3 Extracting Speech from Noise.- 3.3.4 Unsupervised Classification Using ICA.- 3.3.5 Computational Finance.- 3.4 Open Problems for ICA Research.- 3.5 Summary.- References.- Appendix - Selected ICA Resources.- 4 Regulatory Applications of Artificial Intelligence.- 4.1 Introduction.- 4.2 Solution Spaces, Data and Mining.- 4.3 Artificial Intelligence in Context.- 4.4 Anomaly Detection: ANNs for Prediction/Classification.- 4.4.1 Training to Classify on Spare Data Sets.- 4.4.2 Training to Predict on Dense Data Sets.- 4.4.3 Feature Selection for and Performance of Anomaly Detection Suites.- 4.4.4 Interpreting Anomalies.- 4.4.5 Other Approaches to Anomaly Detection.- 4.4.6 Variations of BackProp' ANNs for Use with Complex Data Sets.- 4.5 Formulating Expert Systems to Identify Common Events of Interest.- A Note on the Software.- Acknowledgements.- References.- 5 An Introduction to Collective Intelligence.- 5.1 Collective Intelligence.- 5.1.1 A Simple Example of Stigmergy at Work.- 5.2 The Power of Collective Action.- 5.3 Optimisation.- 5.3.1 Optimisation in General.- 5.3.2 Shades of Optimisation.- 5.3.3 Exploitation versus Exploration.- 5.3.4 Example of Common Optimisation Problems.- Minimum Path Length.- Function Optimisation.- Sorting.- Multi-Component Optimisation.- 5.4 Ant Colony Optimisation.- 5.4.1 Ant Systems - the Basic Algorithm.- The Problem with AS.- 5.4.2 Ant Colony Systems.- 5.4.3 Ant Multi-Tour System (AMTS).- 5.4.4 Limiting the Pheromone Density - the Max-Min Ant System.- 5.4.5 An Example: Using Ants to Solve a (simple) TSP.- 5.4.6 Practical Considerations.- 5.4.7 Adding a Local Heuristic.- 5.4.8 Other Uses for ACO.- 5.4.9 Using Ants to Sort.- An Example of Sorting Using ACO.- 5.5 Particle Swarm Optimisation.- 5.5.1 The Basic Particle Swarm Optimisation Algorithm.- 5.5.2 Limitations
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