迁移学习数据分类中的ESVM算法

ESVM Algorithm in Transfer Learning Data Classification

在迁移学习中对变化后的数据集进行分类时,噪音导致分类结果不合理。为此,提出一种迁移学习数据分类中的扩展支持向量机(ESVM)算法。使用变化前数据集的概率分布信息及学习经验,指导缓慢变化后的数据集进行分类,使分割面既可以准确分割现有数据集,同时也保留原先数据集的一些属性。实验结果表明,该算法具有一定的抗噪性能。

In transfer learning process, noise makes the result unreasonable when you classify slow changing dataset. Here is an algorithm called Extended Support Vector Machine（ESVM） proposed to solve this problem. Because it makes full use of probability distribution of original data and uses the learning experience of the previous dataset to classify the latter dataset, ESVM can correctly classify the changing dataset with inheriting the characteristics from the previous dataset. Experimental result shows the antinoise performance of the algorithm.