基于域与样例平衡的多源迁移学习方法

针对如何有效使用多源域的决策知识去预测目标域样例标签的问题，提出一种平衡域与样例信息的多源迁移学习算法.为实现上述目的，本文提出了一种基于域与样例平衡的多源迁移学习方法（Multi-source Transfer Learning by Balancing both Domains and Instances，MTL-BDI）.该方法的基本思想是将域层面和样例层面的双加权平衡项嵌入到迁移学习的原始目标函数中，然后利用交替优化技术对提出的目标函数进行有效求解.在文本和图像数据集上的大量实验表明，该方法在分类精度方面确实优于现有的多源迁移学习方法MCC-SVM（Multiple Convex Combination of SVM）、A-SVM（Adaptive SVM）、Multi-KMM（Multiple Kernel Mean Matching）和DAM（Domain Adaptation Machine）.

Multi-source Transfer Learning Method by Balancing both the Domains and Instances

When transfer learning attempts to leverage the decision knowledge effectively from multiple source domains to predict the labels of instances accurately in target domain,it should consider how to well balance source and target domains,and their instances in both domains.In this paper,a novel multi-source transfer learning method called mlti-source transfer learning by balancing both domains and instances (MTL-BDI) is proposed to achieve the above goal.The basic idea of the proposed method is to embed the doubly weighted domain-level and instance-level balance term into the original objective function of transfer learning and then solve the proposed objective function effectively by using the alternating optimization technique.Extensive experiments on text and image datasets indicate that the proposed method indeed outperforms several existing multi-source transfer learning methods MCC-SVM (Multiple Convex Combination of SVM),A-SVM (Adaptive SVM),Multi-KMM (Multiple Kernel Mean Matching) and DAM (Domain Adaptation Machine) in the sense of classification accuracy on target domain.