CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
杨雪冰; 张文生; 杨阳
Source Publication山西大学学报(自然科学版)
Other AbstractThe theory of Probably Approximately Correct (PAC) is a framework for the study of learnable. In recent years, researchers combined Bayesian method with distribution-free PAC guarantees and proposed so-called PAC-Bayesian learning theory. This theory has been widely used in different fields of Artificial Intelligence to analyze related algorithms for the given generalization error bounds can apply to an arbitrary prior measure on an arbitrary concept space. This paper surveys the derivation of PAC-Bayesian learning theory and its core ideas. Further, considering the characteristics of big data, this paper discusses why PAC-Bayesian is useful for theoretical analysis of the related algorithms for big data.
KeywordPac-bayesian界 学习理论 贝叶斯学习 大数据
Document Type期刊论文
Corresponding Author张文生
Recommended Citation
GB/T 7714
杨雪冰,张文生,杨阳. 大数据下的PAC-Bayesian学习理论综述[J]. 山西大学学报(自然科学版),2015,38(3):413-419.
APA 杨雪冰,张文生,&杨阳.(2015).大数据下的PAC-Bayesian学习理论综述.山西大学学报(自然科学版),38(3),413-419.
MLA 杨雪冰,et al."大数据下的PAC-Bayesian学习理论综述".山西大学学报(自然科学版) 38.3(2015):413-419.
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