C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning Methods for Ecological Applications
Machine Learning Methods for Ecological Applications
Machine Learning
Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Comparing machine learning classifiers in potential distribution modelling
Expert Systems with Applications: An International Journal
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Potential distribution modelling has been widely used to predict and to understand the geographical distribution of species. These models are generally produced by retrieving the environmental conditions where the species is known to be present or absent and feeding this data into a modelling algorithm. This paper investigates the use of Machine Learning techniques in the potential distribution modelling of plant species Stryphnodendron obovatumBenth (MIMOSACEAE). Three techniques were used: Support Vector Machines, Genetic Algorithms and Decision Trees. Each technique was able to extract a different representation of the relations between the environmental conditions and the distribution profile of the species being considered.