Tree-Based methods. The use of classification trees to predict species distributions Export

@incollection{Bell1999, abstract = {This chapter introduces classification tree methodology as an alternative to methods such as discriminant analysis and logistic regression. The technique is illustrated using a small set of dat on the species-habitat relationships of pronghorn deer. This is analysed using the package QUEST.}, address = {Norwell, Massachusetts}, author = {Bell, John F.}, booktitle = {Machine learning methods for ecological applications}, citeulike-article-id = {502380}, editor = {Fielding, Alan H.}, keywords = {accuracy, analysis, associations, discriminant, eaf, habitat, ipurity, outliers, partitioning, pruning, recursive, regression, tree}, pages = {89--105}, posted-at = {2006-02-12 15:42:56}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {Tree-Based methods. The use of classification trees to predict species distributions}, year = {1999} }

TY - CHAP ID - Bell1999 L3 - citeulike-article-id:502380 N2 - This chapter introduces classification tree methodology as an alternative to methods such as discriminant analysis and logistic regression. The technique is illustrated using a small set of dat on the species-habitat relationships of pronghorn deer. This is analysed using the package QUEST. AD - Norwell, Massachusetts EP - 105 TI - Tree-Based methods. The use of classification trees to predict species distributions PB - Kluwer Academic Publishers T2 - Machine learning methods for ecological applications SP - 89 KW - accuracy KW - analysis KW - associations KW - discriminant KW - eaf KW - habitat KW - ipurity KW - outliers KW - partitioning KW - pruning KW - recursive KW - regression KW - tree AU - Bell, John A2 - Fielding, Alan PY - 1999/// ER -