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Home > Archive>Volume 31, Issue 4, 2020 >981-990. DOI:10.13328/j.cnki.jos.005919
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Weakly Supervised Learning Framework Based on k Labeled Samples
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TP181

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Key Program for Int'l S&T Cooperation of Sichuan Province of China (2019YFH0097)

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    Abstract:

    Clustering is an active research topic in the field of machine learning. Weakly supervised learning is an important research direction in semi-supervised learning, which has wide range of application scenarios. In the research of clustering and weakly supervised learning, it is proposed that a framework of weakly supervised learning is based on k labeled samples. Firstly, the framework expands labeled samples by clustering and clustering confidence level. Secondly, the energy function of the restricted Boltzmann machine is improved, and a learning model of the restricted Boltzmann machine based on k labeled samples is proposed. Finally, the model of ratiocination and algorithm are proposed. In order to test the framework and the model, a series of public data sets are chosen for comparative experiments. The experimental results show that the proposed weakly supervised learning framework based on k labeled samples is more effective.

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付治,王红军,李天瑞,滕飞,张继.基于k个标记样本的弱监督学习框架.软件学报,2020,31(4):981-990

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History
  • Received:March 10,2019
  • Revised:July 11,2019
  • Online: January 14,2020
  • Published: April 06,2020
Links:Institute of Software, CASChina Computer FederationCASNSFC
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