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WEKA: The Waikato Environment for Knowledge Analysis (1995)
| Venue: | In Proc. of the New Zealand Computer Science Research Students Conference |
| Citations: | 84 - 0 self |
BibTeX
@INPROCEEDINGS{Garner95weka:the,
author = {Stephen R. Garner},
title = {WEKA: The Waikato Environment for Knowledge Analysis},
booktitle = {In Proc. of the New Zealand Computer Science Research Students Conference},
year = {1995},
pages = {57--64}
}
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Abstract
WEKA is a workbench designed to aid in the application of machine learning technology to real world data sets, in particular, data sets from New Zealand's agricultural sector. In order to do this a range of machine learning techniques are presented to the user in such a way as to hide the idiosyncrasies of input and output formats, as well as allow an exploratory approach in applying the technology. The system presented is a component based one that also has application in machine learning research and education. 1. Introduction The WEKA machine learning workbench has grown out of the need to be able to apply machine learning to real world data sets in a way that promotes a "what if?..." or exploratory approach. Each machine learning algorithm implementation requires the data to be present in its own format, and has its own way of specifying parameters and output. The WEKA system was designed to bring a range of machine learning techniques or schemes under a common interface so that t...
