Semi-Supervised Learning with the help of Parzen Windows

JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS

卷:386

Article

Lv, Shao-Gao¹  Feng, Yun-Long^2,3 

[1] SW Univ Finance & Econ, Sch Stat, Chengdu 611130, Peoples R China

[2] Univ Sci & Technol China, Joint Adv Res Ctr, Suzhou 215123, Peoples R China

[3] City Univ Hong Kong, Suzhou 215123, Peoples R China

关键词: Semi-Supervised Learning;    Graph-based models;    Support vector machine;    Least square regression;    Reproducing kernel Hilbert spaces;   

DOI  :  10.1016/j.jmaa.2011.07.059

来源: Elsevier

【 摘 要 】

Semi-Supervised Learning is a family of machine learning techniques that make use of both labeled and unlabeled data for training, typically a small amount of labeled data with a large number of unlabeled data. In this paper we propose a Semi-Supervised regression algorithm by means of density estimator, generated by Parzen Windows functions under the framework of Semi-Supervised Learning. We conduct error analysis by capacity independent technique and obtain some satisfactory learning rates in terms of regularity of the target function and the decay condition on the marginal distribution near the boundary. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

Free