Work

Multi-Class Learning using Unlabeled Samples: Theory and Algorithm

Year: 2019

Type: conference-paper

Abstract: In this paper, we investigate the generalization performance of multi-class classification, for which we obtain a shaper error bound by using the notion of local Rademacher complexity and additional unlabeled samples, substantially improving the state-of-the-art bounds in existing multi-class learning methods. The statistical learning motivates us to devise an efficient multi-class learning framework with the local Rademacher complexity and Laplacian regularization. Coinciding with the theoretical analysis, experimental results demonstrate that the stated approach achieves better performance.

Source: International Joint Conference on Artificial Intelligence

DOI: 10.24963/ijcai.2019/399

Authors: Jian Li, Yong Liu, Rong Yin, Weiping Wang

Institutions: Chinese Academy of Sciences, Institute of Information Engineering, University of Chinese Academy of Sciences

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, Youth Innovation Promotion Association of the Chinese Academy of Sciences, National Key Research and Development Program of China, Youth Innovation Promotion Association

Awards: G159345655, G3119611672, G6721343884