Publication: A development environment for predictive modelling in foods
| dc.contributor.author | Holmes, Geoffrey | |
| dc.contributor.author | Hall, Mark A. | |
| dc.date.accessioned | 2008-10-13T03:47:01Z | |
| dc.date.available | 2008-10-13T03:47:01Z | |
| dc.date.issued | 2000-07 | |
| dc.description.abstract | WEKA (Waikato Environment for Knowledge Analysis) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning/data mining algorithms. Non-programmers interact with the software via a user interface component called the Knowledge Explorer. Applications constructed from the WEKA class libraries can be run on any computer with a web browsing capability, allowing users to apply machine learning techniques to their own data regardless of computer platform. This paper describes the user interface component of the WEKA system in reference to previous applications in the predictive modeling of foods. | en_US |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Holmes, G. & Hall, M.A. (2000). Correlation-based feature selection of discrete and numeric class machine learning. (Working paper 00/09). Hamilton, New Zealand: University of Waikato, Department of Computer Science. | en_US |
| dc.identifier.issn | 1170-487X | |
| dc.identifier.uri | https://hdl.handle.net/10289/1025 | |
| dc.language.iso | en | |
| dc.publisher | University of Waikato, Department of Computer Science | en_US |
| dc.relation.ispartofseries | Computer Science Working Papers | |
| dc.subject | computer science | en_US |
| dc.subject | Machine learning | |
| dc.title | A development environment for predictive modelling in foods | en_US |
| dc.type | Working Paper | en_US |
| dspace.entity.type | Publication | |
| pubs.place-of-publication | Hamilton | en_NZ |
| uow.relation.series | 00/09 |