This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems.
Table of Contents
- Parameter Optimisation Algorithms.
- Density Modelling and Clustering.
- Single-Layer Networks.
- The Multi-Layer Perceptron.
- Radial Basis Functions.
- Visualisation and Latent Variable Models.
- Bayesian Techniques.
- Gaussian Processes.
- Linear Algebra and Matrices.
- Algorithm Error Analysis.
- Function Index.
- Subject Index.
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