Machine Learning Methods for Ecological Applications / Edition 1

Machine Learning Methods for Ecological Applications / Edition 1

by Alan H. Fielding
     
 

ISBN-10: 0412841908

ISBN-13: 9780412841903

Pub. Date: 08/31/1999

Publisher: Springer US

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

Overview

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.

Product Details

ISBN-13:
9780412841903
Publisher:
Springer US
Publication date:
08/31/1999
Edition description:
1999
Pages:
261
Product dimensions:
6.10(w) x 9.25(h) x 0.03(d)

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. 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.

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