Machine Learning Methods for Ecological ApplicationsAlan Fielding Springer Science & Business Media, 31 ago 1999 - 261 pagine 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. |
Sommario
An introduction to machine learning methods | 1 |
Artificial neural networks for pattern recognition | 37 |
Treebased methods | 89 |
Genetic Algorithms I | 107 |
Genetic Algorithms II | 123 |
Cellular automata | 145 |
Equation discovery with ecological applications | 185 |
How should accuracy be measured? | 209 |
Real learning | 225 |
247 | |
255 | |
Altre edizioni - Visualizza tutto
Machine Learning Methods for Ecological Applications Alan H. Fielding Anteprima non disponibile - 2012 |
Parole e frasi comuni
accuracy analysis animal applied approach areas artificial neural networks assessment automaton BEAGLE behaviour biological Boddy cells cellular automata cellular model Cerulean Warbler Chapter classification tree cluster complex context free grammar correlation costs data set derived discovery systems discriminant distribution dynamics Ecological Modelling ecosystem equation discovery systems error rate Euclidean distance example experiments Figure flow cytometry forest function GARP genetic algorithm GOLDHORN Greater Glider habitat HLNs identification individual input interactions iteration Journal kernel Kohonen LAGRANGE landmarks landscape layer linear logistic regression machine learning measured ML methods neighbourhood nest obtained output parameters partitioning patches performance phyt phytoplankton place cells plants plot population possible potential prediction predictor present probability problem random range RBF ANNs relationships response sample selection simple simulation solution spatial patterns species split structure success Table techniques tessellation test data threshold training data values variables vector vegetation weights
Brani popolari
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Riferimenti a questo libro
Fundamentals of Ecological Modelling Sven Erik Jørgensen,G. Bendoricchio Anteprima non disponibile - 2001 |
Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt,Antonello Pasini,Caren Marzban Anteprima limitata - 2008 |