Advances in Machine Learning & Artificial Intelligence(AMLAI)

School of Computer Science and Communication Engineering, Guangxi University of Science and Technolo, China

Publications

• Research Article   
  AI Uncertainty Based on Rademacher Complexity and Shannon Entropy
  Author(s): Mingyong Zhou*
  
  In this paper from communication channel coding perspective we are able to present both a theoretical and practical discussion of AI’s uncertainty, capacity and evolution for pattern classification based on the classical Rademacher complexity and Shannon entropy. First AI capacity is defined as in communication channels. It is shown qualitatively that the classical Rademacher complexity and Shannon rate in communication theory is closely related by their definitions. Secondly based on the Shannon mathematical theory on communication coding, we derive several sufficient and necessary conditions for an AI’s error rate approaching zero in classifications problems. A 1/2 criteria on Shannon entropy is derived in this paper so that error rate can approach zero or is zero for AI pattern classification problems. Last but not least, we show our analysis and theory by providing exa.. Read More»
  

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