Machine Learning Methods for Ecological Applications 1999 edition

Marc Notes: Includes bibliographical references and indexes. Review Quotes: I believe this book is a very useful contribution and an excellent starting point for ecologists who are interested in applying machine learning methods to ecological problems.' Uygar Ozesmi in Ecology, 81:9 (2000)"Table of Contents: 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. Stockwell. 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. Publisher Marketing: The last 25 years have seen a tremendous growth in the application of statistical and modelling techniques to ecological problems. This expansion 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 problems are at best poorly defined and most lack algorithmic solutions. Machine learning methods offer the potential for a different approach to these difficult problems.

Contributor Bio:  Fielding, Alan H Alan H. Fielding is Senior Lecturer in the Division of Biology at Manchester Metropolitan University.