Sinossi
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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Recensione
`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)
Contenuti
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.
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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 particul. Codice articolo 5915340
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Buch. Condizione: Neu. Machine Learning Methods for Ecological Applications | Alan H. Fielding | Buch | xiii | Englisch | 1999 | Springer US | EAN 9780412841903 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Codice articolo 102563708
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Condizione: New. The aim of this text is to introduce machine learning methods to professional ecologists. Topics covered in the text include the identification of species, optimal mate choice, predicting species distributions and modelling landscape features. Editor(s): Fielding, Alan H. Num Pages: 261 pages, biography. BIC Classification: RNC. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 17. Weight in Grams: 576. . 1999. Hardback. . . . . Codice articolo V9780412841903
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 284 pp. Englisch. Codice articolo 9780412841903
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