Projects per year
Recently, ordinal regression, which predicts categories of ordinal scale, has received considerable attention. In this paper, we propose a new approach to solve ordinal regression problems within the learning vector quantization framework. It extends the previous approach termed ordinal generalized matrix learning vector quantization with a more suitable and natural cost function, leading to more intuitive parameter update rules. Moreover, in our approach the bandwidth of the prototype weights is automatically adapted. Empirical investigation on a number of datasets reveals that overall the proposed approach tends to have superior out-of-sample performance, when compared to alternative ordinal regression methods.
|Early online date||9 May 2017|
|Publication status||Published - 1 Sept 2017|
- Ordinal regression
- Learning vector quantization
- Generalized matrix learning vector quantization
FingerprintDive into the research topics of 'Ordinal regression based on learning vector quantization'. Together they form a unique fingerprint.
- 1 Finished
Personalised Medicine through Learning in the Model Space
Engineering & Physical Science Research Council
1/10/13 → 31/03/17
Project: Research Councils