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面向共享数据的迁移组概率学习机

DOI: 10.13195/j.kzyjc.2013.0376, PP. 1363-1371

Keywords: 迁移学习,分类,支持向量机,共享数据

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Abstract:

为了解决机器学习中的主观信息缺失问题,提出一种新的面向共享数据的迁移组概率学习机(TGPLM-CD).该方法基于结构风险最小化模型,将源领域所含知识和目标领域的类标签组概率信息,特别是领域间的共享数据纳入学习框架中,实现了源领域和目标领域的知识迁移,在待研究领域数据信息不足的情况下提高了分类精确度.大量数据集上的实验结果验证了所提出方法的有效性.

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