A Neural Network Model for Online Multi-Task Multi-Label Pattern Recognition-猫眼课题宝

DOI：

10.1007/978-3-642-40728-4_21

发表时间：

2013-09

期刊：

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其他

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作者：

D. Higuchi;S. Ozawa

本文提出了一种新的顺序多任务学习模型，该模型具有单次增量学习、任务分配、知识迁移、任务整合、多标签数据学习和主动学习等功能。该模型对任务信息不完整的多标签数据进行增量学习。当未给出任务信息时，根据预测误差给相应的任务分配类标签；因此，任务分配有时会失败，特别是在早期阶段。为了从错误分配中恢复，该模型具有一种称为任务整合的备份机制，该机制不仅可以根据预测误差修改任务分配，还可以根据训练数据中的任务标签（如果给定）和多标签数据的启发式来修改任务分配。实验结果表明，该模型在分类和任务分类方面都具有良好的性能。

This paper presents a new sequential multi-task learning model with the following functions:one-pass incremental learning,task allocation,knowledge transfer,task consolidation,learning of multi-label data, andactive learning. This model learns multi-label data with incomplete task information incrementally. When no task information is given, class labels are allocated to appropriate tasks based on prediction errors; thus, the task allocation sometimes fails especially at the early stage. To recover from the misallocation, the proposed model has a backup mechanism called task consolidation, which can modify the task allocation not only based on prediction errors but also based on task labels in training data (if given) and a heuristics on multi-label data. The experimental results demonstrate that the proposed model has good performance in both classification and task categorization.

A Neural Network Model for Online Multi-Task Multi-Label Pattern Recognition

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