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Jun, 2017
用于受监督域自适应的耦合支持向量机
Coupled Support Vector Machines for Supervised Domain Adaptation
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Hemanth Venkateswara, Prasanth Lade, Jieping Ye, Sethuraman Panchanathan
TL;DR
本文提出了一种基于支持向量机的监督领域适应技术,通过模拟源域和目标域之间的支持向量机决策边界的相似性,将源支持向量机和目标支持向量机相耦合,并将模型简化为标准的单支持向量机。我们在多个数据集上测试耦合支持向量机,并将结果与其他流行的支持向量机领域适应方法进行了比较。
Abstract
Popular
domain adaptation
(DA) techniques learn a
classifier
for the target domain by sampling relevant data points from the source and combining it with the target data. We present a Support Vector Machine (SVM)
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