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| 科研成果 |
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| 论文题目： |
Ordinal regression based on learning vector quantization |
| 第一作者： |
Tang FZ(唐凤珍);Tio, Peter |
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| 发表刊物： |
Neural Networks |
| 发表年度： |
2017 |
| 卷,期,页： |
93,,76-88 |
| 论文出处： |
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| 论文编号： |
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| 论文摘要： |
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. |
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