Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
IEEE Trans Pattern Anal Mach Intell. 2010 May;32(5):770-87. doi: 10.1109/TPAMI.2009.57.
This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.
本文在域自适应框架中讨论模式分类问题，考虑了仅在源域（即使相关）存在训练数据而在目标域（无标签）测试数据的情况下解决问题的方法。本文提出了两个主要的新颖贡献：1）一种域自适应支持向量机（DASVM）技术，它将支持向量机（SVM）的公式扩展到域自适应框架中；2）一种圆形间接准确性评估策略，用于在没有目标域实例的真实标签的情况下验证域自适应分类器的学习。在一系列二维玩具问题和两个与脑机接口和遥感应用相关的真实数据集上获得的实验结果证实了 DASVM 技术和所提出的圆形验证策略的有效性和可靠性。
