Investigation of Generalised Nearest Neighbour in Machine Learning
| dc.contributor.author | MITCHELL, James | |
| dc.date.accessioned | 2017-12-05T02:56:09Z | |
| dc.date.available | 2017-12-05T02:56:09Z | |
| dc.date.issued | 2004 | en |
| dc.description.abstract | Instance-based learning is a machine learning that classifies new examples by comparing them to previously seen examples. Non Nested Generalised Exemplars is one such learning algorithm which combines generalisation to provide support for large and small disjuncts. This paper looks at improving this learners tolerance to noise, introducing several possible techniques. Problems were encounted in the implementation of the extensions, preventing the study of the effect of the extensions. | en |
| dc.identifier.uri | http://hdl.handle.net/10092/14809 | |
| dc.identifier.uri | http://dx.doi.org/10.26021/1320 | |
| dc.language | English | |
| dc.language.iso | en | |
| dc.publisher | University of Canterbury | en |
| dc.rights | All Right Reserved | en |
| dc.rights.uri | https://canterbury.libguides.com/rights/theses | en |
| dc.title | Investigation of Generalised Nearest Neighbour in Machine Learning | en |
| dc.type | Theses / Dissertations | en |
| thesis.degree.grantor | University of Canterbury | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Other | en |
| uc.college | Faculty of Engineering | en |