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Information Theory and Its Relation to Machine Learning
Hu, Bao-Gang
Conference NameChinese Intelligent Automation Conference
Conference Date2015
Conference PlaceFuzhou, China
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. 
KeywordMachine Learning Learning Target Selection Entropy Information Theory Similarity Conjecture
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Corresponding AuthorHu, Bao-Gang
AffiliationNational Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hu, Bao-Gang. Information Theory and Its Relation to Machine Learning[C],2015.
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