Classification problems are important in medical diagnosis. In this workshop, we present and summarize our recent insights into AI uncertainty through classification problems from both theoretical and empirical perspectives. First a concept and a theory are proposed aiming at zero-error AI system. We show in fact they can be derived from Shannon communication theory based on entropy concept. Classical Rademacher complexity and Shannon entropy is shown to be closely related by quantity by definitions. Based on this observation, we are able to derive a 1/2 criteria in terms of Shannon entropy that guarantee an AI zero-error accuracy in classifications problems. Last but not the least, we show both a relaxing condition and a stricter condition in real applications that can guarantee zero-error accuracy in AI classification problems. We provide examples to show in applications how to apply our derived 1/2 criteria into AI applications by “coding” methods.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com