1 Multi-Layer Perceptron Training --;1.1 Introduction to MLPs --;1.2 Error Surfaces and Local Minima --;1.3 Backpropagation --;2 Classical Optimisation --;2.1 Introduction to Classical Methods --;2.2 General Numerical Considerations --;3 Second-Order Optimisation Methods --;3.1 Line-Search Strategies --;3.2 Model-Trust Region Strategies --;3.3 Multivariate Methods for General Nonlinear Optimisation --;3.4 Special Methods for Nonlinear Least Squares --;3.5 Comparison of Methods --;4 Second-Order Training Methods for MLPs --;4.1 The Calculation of Second Derivatives --;4.2 Reducing Storage and Computational Costs --;4.3 Second-Order On-Line Training --;4.4 Conclusion --;5 An Experimental Comparison of MLP Training Methods --;5.1 Benchmark Training Tasks --;5.2 Initial Training Conditions --;5.3 Experimental Results --;6 Global Optimisation --;6.1 Introduction to Global Methods --;6.2 Expanded Range Approximation (ERA) --;6.3 The TRUST Method.
SUMMARY OR ABSTRACT
Text of Note
About This Book This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs).