Matovu, M. (2010) Adaptive feature space transformation in Generalized Matrix Learning Vector Quantization. Master's Thesis / Essay, Computing Science.
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Abstract
We propose and investigate a modification of Generalized Matrix Relevance Learning Vector Quantization (GMLVQ). In the novel approach we restrict the linear transformation to only the data set instead of transforming both the prototypes and the data like in the original GMLVQ. The method is implemented using a rectangular relevance matrix in a modified Euclidean distance measure. We analyse the performance of the modified algorithm and compare with original GMLVQ. In this talk, the method is outlined and experimental results are discussed in terms of a benchmark classification task.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:30 |
| Last Modified: | 15 Feb 2018 07:30 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/9045 |
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