Information Theory and Its Relation to Machine Learning
Hu, Bao-Gang
2015
会议名称Chinese Intelligent Automation Conference
会议日期2015
会议地点Fuzhou, China
摘要
In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely “What to learn?”, “How to learn?”, “What to evaluate?”, and “What to adjust?”. The paper stresses more on the first level of “What to learn?”, or “Learning Target Selection”. Toward this primary problem within the four levels, I briefly review the existing studies about the connection between information theoretical learning (ITL [1]) and machine
learning. A theorem is given on the relation between the empirically-defined similarity measure and information measures. Finally, a conjecture is proposed for pursuing a unified mathematical interpretation to learning target selection. 
关键词Machine Learning Learning Target Selection Entropy Information Theory Similarity Conjecture
DOI10.1007/978-3-662-46469-4_1
引用统计
被引频次:2[WOS]   [WOS记录]     [WOS相关记录]
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/20008
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Hu, Bao-Gang
作者单位National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
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GB/T 7714
Hu, Bao-Gang. Information Theory and Its Relation to Machine Learning[C],2015.
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