Machine Learning Methods for Ecological Applications / Edition 1
The last 25 years have seen a tremendous growth in the application of statistical and modelling techniques to ecological problems. This expa nsion has been accelerated by the increasing availability of software, books and computing power. However, the suitability of some of these approaches to data analysis, in a relatively knowledge-poor discipline such as ecology, can be questioned on grounds of appropriateness and robustness. One reason for these concerns is that many ecological prob lems are at best poorly defined and most lack algorithmic solutions. M achine learning methods offer the potential for a different approach t o these difficult problems.
Contributors. Preface. Acknowledgements. 1. An introduction to machine learning methods; A. Fielding. 2. Artificial neural networks for pattern recognition; L. Boddy, C.W. Morris. 3. Tree-based methods; J.F. Bell. 4. Genetic Algorithms I; J.N.R. Jeffers. 5. Genetic Algorithms II; D.R.B. Skwell. 6. Cellular automata; D. Dunkerley. 7. Equation discovery with ecological applications; S. Szeroski, et al. 8. How should accuracy be measured? A. Fielding. 9. Real learning; B. Stevens-Wood. Author Index. Subject Index.