基于抗噪声的多任务多示例学习算法研究

在多示例学习中，当训练样本数量不充足或者训练样本中存在噪声信息时，分类器的分类性能将降低.针对该问题，本文提出了一种基于抗噪声的多任务多示例学习算法.一方面，针对训练样本中可能存在的噪声问题，该算法赋予包中示例不同的权值，通过迭代更新权值来降低噪声数据对预测结果的影响.另一方面，针对训练样本数量不充足问题，该算法运用多任务学习策略，通过同时训练多个学习任务，利用任务间的关联性来提高各个分类任务的预测性能.实验结果证明，与现有的分类算法相比，该方法在相同的实验条件下具有更优秀的性能.

An Algorithm Based on Multi-task Multi-instance Anti-noise Learning

In multi-instance learning, classification performance may be limited due to the noisy data or a scarce amount of labeled data. To solve this problem, an algorithm based on multi-task multi-instance anti-noise learning is proposed. On the one hand, in view of the noisy data, the algorithm trains a classifier by assigning the instances in bags with different weights. And the weights of instances are updated by adopting an iterative optimization framework which decreases the influence of the noisy data. On the other hand, in view of insufficient labeled data, the classifier is extended to multi-task learning to train multiple learning tasks at the same time, so that the performance of each learning task can be improved by sharing the classification information among the tasks. Extensive experiments have showed that the proposed classification framework outperforms the existing classification methods.