Machine Learning Methods for Ecological Applications (inbunden)

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Format: Inbunden (Hardback)
Språk: Engelska
Antal sidor: 261
Utgivningsdatum: 1999-08-01
Upplaga: 1999 ed.
Förlag: Chapman and Hall
Medarbetare: Fielding, Alan H. (ed.)
Illustrationer: XIII, 261 p.
Dimensioner: 245 x 164 x 20 mm
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Antal komponenter: 1
Komponenter: 1 Hardback
ISBN: 9780412841903

Machine Learning Methods for Ecological Applications

Name: Machine Learning Methods for Ecological Applications
Price: 2168.00 SEK
Availability: InStock
Author: Alan H Fielding
ISBN: 9780412841903

av Alan H Fielding

Inbunden, Engelska, 1999-08-01

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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.

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Cluster and Classification Techniques for the Biosciences

Alan H Fielding

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more wid...

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`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 zesmi in Ecology, 81:9 (2000)

1. 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.