A Sequential Multitask Learning Algorithm for Pattern Recognition
A Sequential Multitask Learning Algorithm for Pattern Recognition
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一种用于模式识别的顺序多任务学习算法
DOI：
10.1109/devlrn.2012.6400827
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
and S. Ozawa
中科院分区：
文献类型：
--
作者：
T. Takata;D. Higuchi;and S. Ozawa
在这项工作中，我们扩展了顺序多任务学习模型称为资源分配网络多任务模式识别（RAN-MTPR）通过引入以下新的学习功能：多标签识别，半监督任务学习和主动学习。扩展的RAN-MTPR可以学习具有多个类标签的训练数据，可以处理任务学习的半监督设置，并且可以主动请求不确定输入的类标签。我们评估了扩展的RAN-MTPR的性能，我们知道，上述三个功能很好地工作，以提高模式识别问题的泛化性能。
In this work, we extend the sequential multitask learning model called Resource Allocating Network for Multi-Task Pattern Recognition (RAN-MTPR) by introducing the following new learning functions: multi-label recognition, semi-supervised task learning and active learning. The extended RAN-MTPR can learn a training data with multiple class labels, can handle a semi-supervised setting for task learning, and can actively request class labels for unsure inputs. We evaluate the performance of the extended RAN-MTPR, and we know that the above three functions work well to enhance the generalization performance for pattern recognition problems.