Abstract
摘要: 现有的领域自适应方法在定义领域间分布距离时, 通常仅从领域样本的整体分布上考虑, 而未对带类标签的领域样本分布分别进行考虑, 从而在一些具有非平衡数据集的应用领域上表现出一定的局限性. 对此, 在充分考虑源领域样本类信息的基础上, 基于结构风险最小化模型, 提出了基于类分布的领域自适应支持向量机(Domain adaptation support vector machine based on class distribution, CDASVM), 并将其拓展为可处理多源问题的多源领域自适应支持向量机(CDASVM from multiple sources, MSCDASVM), 在人造和真实的非平衡数据集上的实验结果表明, 所提方法具有优化或可比较的模式分类性能. 关键词: 领域自适应 / 支持向量机 / 迁移学习 / 再生核Hilbert空间
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
