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OverviewThe 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. One definition of machine learning is that it is concerned with inducing knowledge from data, where the data could be patterns in a game of chess or patterns in the species composition of natural communities. Unfortunately ecologists have little experience of these relatively recent and novel approaches to understanding data. This is a problem that is made more complex because there is no simple taxonomy of machine learning methods and there are relatively few examples in the mainstream ecological literature to encourage exploration. This is the first text aimed at introducing machine learning methods to a readership of professional ecologists. All but one of the chapters have been written by ecologists and biologists who highlight the application of a particular method to a particular class of problem. Examples include the identification of species, optimal mate choice, predicting species distributions and modelling landscape features. A group of experienced machine learning workers, who have become interested in environmental problems, have written a chapter that demonstrates how machine learning methods can be used to discover equations that describe the dynamic behaviour of ecological systems. The final chapter reviews `real learning', offering the potential for greater dialogue between the biological and machine learning communities. Full Product DetailsAuthor: Alan H. FieldingPublisher: Chapman and Hall Imprint: Chapman and Hall Edition: 1999 ed. Dimensions: Width: 15.50cm , Height: 1.70cm , Length: 23.50cm Weight: 1.270kg ISBN: 9780412841903ISBN 10: 0412841908 Pages: 261 Publication Date: 31 August 1999 Audience: College/higher education , Professional and scholarly , Postgraduate, Research & Scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1. An introduction to machine learning methods.- 2. Artificial neural networks for pattern recognition.- 3. Tree-based methods.- 4. Genetic Algorithms I.- 5. Genetic Algorithms II.- 6. Cellular automata.- 7. Equation discovery with ecological applications.- 8. How should accuracy be measured?.- 9. Real learning.- Author Index.ReviewsI 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 A-zesmi in Ecology, 81: 9 (2000) '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) '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 A-zesmi in Ecology, 81:9 (2000) Author InformationTab Content 6Author Website:Countries AvailableAll regions |
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