Graph-Based Semi-Supervised Learning on Evolutionary Data

被引：0

作者：

Song, Yanglei ^{[1
]}

Yang, Yifei ^{[2
]}

Dou, Weibei ^{[1
]}

Zhang, Changshui ^{[2
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China

来源：

INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: BIG DATA AND MACHINE LEARNING TECHNIQUES, ISCIDE 2015, PT II | 2015年 / 9243卷

关键词：

Machine learning; Semi-supervised learning; GSSLE;

D O I：

10.1007/978-3-319-23862-3_46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a graph based semi-supervised learning algorithm on evolutionary data. By applying evolutionary smoothness assumption and incorporating it to the general framework of graph-based semi-supervised learning, we got a new algorithm called GSSLE. Empirical evaluations show that our method outperforms other state-of-the-art methods in terms of stability. It is able to deal with dynamic feature space tasks and proves efficient even if we do not have many unlabeled samples in the semi-supervised procedure.

引用

页码：467 / 476

页数：10

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