首页|Safe semi-supervised learning: a brief introduction

Safe semi-supervised learning: a brief introduction

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Semi-supervised learning constructs the predictive model by learning from a few labeled training examples and a large pool of unlabeled ones.It has a wide range of application scenarios and has attracted much attention in the past decades.However,it is noteworthy that although the learning performance is expected to be improved by exploiting unlabeled data,some empirical studies show that there are situations where the use of unlabeled data may degenerate the performance.Thus,it is advisable to be able to exploit unlabeled data safely.This article reviews some research progress of safe semi-supervised learning,focusing on three types of safeness issue:data quality,where the training data is risky or of low-quality;model uncertainty,where the learning algo-rithm fails to handle the uncertainty during training;measure diversity,where the safe performance could be adapted to diverse measures.

machine learningsemi-supervised learningsafe

Yu-Feng LI、De-Ming LIANG

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National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China

This research was supported by the National Key R&D Program of ChinaNational Natural Science Foundation of Chinaand the Fundamental Research Funds for the Central Universities

2017YFB1001903Grant No.61772262

2019

计算机科学前沿
高等教育出版社

计算机科学前沿

CSTPCDCSCDSCIEI
影响因子：0.303
ISSN：2095-2228
年,卷(期)：2019.13(4)
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