基于相似领域共享特征的分类学习模型

Classification model based on common features between similar domains

传统上下文在分类研究中通常存在失真和有效性等问题。引入研究对象领域的相似领域作为上下文,借助迁移学习理论,使用结构化相似性学习方法构建研究对象领域和其相似领域间的低维共享特征,提出一种基于相似领域共享特征的分类学习模型。实验以QQ空间的个性化设置数据作为上下文,对用户电子商务网站页面的风格偏好进行分类,验证了所提模型的可行性和有效性。

Distortion and low efficiency are two constant problems when employing traditional context in classification problems. Inspired by the transfer learning theory, the paper regards the similar domain of the target domain as context, and constructs the low-dimensional common features between the target domain and its similar domain by structural corre-spondence learning method. Based on the common features between similar domains, the paper puts forward a new classi-fication model. The experiment employs users’personalized options of QQ-zone as context to classify users’preferences of e-commerce web pages, the results verify the feasibility and availability of the proposed model.