Publication: Weka: Practical machine learning tools and techniques with Java implementations
| dc.contributor.author | Witten, Ian H. | |
| dc.contributor.author | Frank, Eibe | |
| dc.contributor.author | Trigg, Leonard E. | |
| dc.contributor.author | Hall, Mark A. | |
| dc.contributor.author | Holmes, Geoffrey | |
| dc.contributor.author | Cunningham, Sally Jo | |
| dc.date.accessioned | 2008-10-17T03:38:34Z | |
| dc.date.available | 2008-10-17T03:38:34Z | |
| dc.date.issued | 1999-08 | |
| dc.description.abstract | The Waikato Environment for Knowledge Analysis (Weka) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning and data mining algorithms. Weka is freely available on the World-Wide Web and accompanies a new text on data mining [1] which documents and fully explains all the algorithms it contains. Applications written using the Weka class libraries can be run on any computer with a Web browsing capability; this allows users to apply machine learning techniques to their own data regardless of computer platform. | en_US |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Witten, I.H., Frank, E., Trigg, L., Hall, M., Holmes, G. & Cunningham, S.J. (1999). Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science. | en_US |
| dc.identifier.uri | https://hdl.handle.net/10289/1040 | |
| dc.language.iso | en | |
| dc.relation.ispartofseries | Computer Science Working Papers | |
| dc.subject | computer science | en_US |
| dc.subject | Machine learning | |
| dc.title | Weka: Practical machine learning tools and techniques with Java implementations | en_US |
| dc.type | Working Paper | en_US |
| dspace.entity.type | Publication | |
| uow.relation.series | 99/11 |