TY - JOUR
T1 - Open-source Machine Learning: R Meets Weka
AU - Hornik, Kurt
AU - Buchta, Christian
AU - Zeileis, Achim
PY - 2009/9/1
Y1 - 2009/9/1
N2 - Two of the prime open-source environments available for machine/statistical learning in data mining and knowledge discovery are the software packages Weka and R which have emerged from the machine learning and statistics communities, respectively. To make the different sets of tools from both environments available in a single unified system, an R package RWeka is suggested which interfaces Wekas functionality to R. With only a thin layer of (mostly R) code, a set of general interface generators is provided which can set up interface functions with the usual R look and feel, re-using Wekas standardized interface of learner classes (including classifiers, clusterers, associators, filters, loaders, savers, and stemmers) with associated methods.
AB - Two of the prime open-source environments available for machine/statistical learning in data mining and knowledge discovery are the software packages Weka and R which have emerged from the machine learning and statistics communities, respectively. To make the different sets of tools from both environments available in a single unified system, an R package RWeka is suggested which interfaces Wekas functionality to R. With only a thin layer of (mostly R) code, a set of general interface generators is provided which can set up interface functions with the usual R look and feel, re-using Wekas standardized interface of learner classes (including classifiers, clusterers, associators, filters, loaders, savers, and stemmers) with associated methods.
UR - http://www.springerlink.com/content/f85818rl04tw1t54/
M3 - Journal article
SN - 0943-4062
VL - 24
SP - 225
EP - 232
JO - Computational Statistics
JF - Computational Statistics
IS - 2
ER -