Machine Learning Methods for Ecological Applications

ISBN-13: 9780412841903 / Angielski / Twarda / 1999 / 261 str.

Alan Fielding; Alan H. Fielding; Alan H. Fielding

Opis

It is difficult to become an ecologist withou, t acquiring some breadth For example, we are expected to be competent statisticians and taxonomists who appreciate the importance of spatial and temporal processes, whilst recognising the potential offered by techniques such as RAPD. It is, therefore, with some trepidation that we offer a collection of potentially useful methods that will be unfamiliar, and possibly alien, to most ecologists. I don't feel old, but when I was undertaking my postgraduate research our lab calculator was mechanical. There was great excitement in my fmal year when we obtained an unbelievably expensive electronic calculator. Later I progressed to running obs' on a PRIME minicomputer via a collection of punched cards. Those who complain about the problems with current computers don't know how lucky they are In 1984 I wrote a book entitled 'Computing for Biologists'. Although it was mainly concerned with writing short programs it did also look at wider aspects of the role of computers in the biological sciences. Machine learning was not mentioned in that book, probably because of ignorance but also because the methods were relatively unknown outside of the relatively small number of workers in the broad field that is now known as machine learning. During 1985 I spent a sabbatical year at York University, following their Biological Computation masters programme. This course was a unique blend of computer science, mathematics and statistics.

Recenzje

`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 Özesmi in Ecology, 81:9 (2000)

Spis treści

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.