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

作　　者：徐俊芬[1,2] 叶俊杰[1,2] 刘业政[1,2] 

机构地区：[1]合肥工业大学管理学院,合肥230009 [2]过程优化与智能决策教育部重点实验室,合肥230009

出　　处：《计算机工程与应用》2014年第17期137-141,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.71071047);高等学校博士学科点专项科研基金(No.20090111110016)

摘　　要：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.

关 键 词：分类 相似领域 上下文 共享特征 特征迁移学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]