基于DLSR的归纳式迁移学习

DLSR based inductive transfer learning method

传统机器学习方法的有效性依赖于大量的有效训练数据,而这难以满足,因此迁移学习被广泛研究并成为近年来的研究热门.针对由于训练数据严重不足导致多分类场景下分类性能降低的挑战,提出一种基于DLSR(discriminative least squares regressions)的归纳式迁移学习方法(TDLSR).该方法从归纳式迁移学习出发,通过知识杠杆机制,将源域知识迁移到目标域并同目标域数据同时进行模型学习,在提升分类性能的同时保证源域数据的安全性.TDLSR继承了DLSR在多分类任务中扩大类别间间隔的优势,为DLSR注入了迁移能力以适应数据不足的挑战,更加适用于复杂的多分类任务.通过在12个真实UCI数据集上进行实验,验证了所提出方法的有效性.

Since the effectiveness of traditional machine learning methods depends on a large amount of effective training data and it is difficult to satisfy,transfer learning has been widely studied and become a hot research in recent years.In order to meet the challenge that the classification performance is degraded due to the serious shortage of training data in current multiclass classification scenarios,a discriminative least squares regressions(DLSR)based inductive transfer learning method(TDLSR)is proposed.The proposed method starts with inductive transfer learning,and transfers knowledge from source domain to target domain through knowledge leverage mechanism.It combines the knowledge of source domain and data in target domain for model learning,which improves classification performance and ensures the security of source domain data.The TDLSR inherits the advantage of the DLSR,which is better applicable to multiclass classification tasks by enlarging the distance between different classes,and injects transfer ability for the DLSR to adapt to the challenge of training data shortage.It can be well applied to various complex multiclass classification tasks.Experiments on 12 real UCI datasets verify the effectiveness of the proposed method.