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

Abstract

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, and active 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.