基于任务权重自动优化的多任务序数回归算法.

At present, there are only a very few works done on multi-task ordinal regression(OR). These works assume that different tasks contribute equally to the overall model. However, in practice, different tasks may have distinct contributions to the overall model. This paper proposed a novel multi-task ordinal regression method with task weight discovery method. Firstly, it presented a support-vector-machine-based multi-task OR model. By sharing the classifier parameters, the classification information could be transferred among different tasks. Secondly, considering that different tasks had different contributions to the overall model, it assigned each task a weight, which would be automatically optimized during the learning process. Finally, it adopted a heuristic framework to construct the multi-task OR model and optimized the task weights alternately. The experimental results show that the proposed method achieves 3.8% to 12.3% improvements in terms of MZE and 4.1% to 11% improvements in terms of MAE, compared to the existing multi-task OR methods. Considering the different weights of each task, and by automatically optimizing these weights, the proposed method reduces the classification error of the multi-task ordinal regression model. [ABSTRACT FROM AUTHOR]