Analogy-based entropic similarity between observations and/or predictions (AESOP): an entropically weighted information-theoretic measure for benchmarking computational accuracy

Salvador Eugenio C. Caoili. Analogy-based entropic similarity between observations and/or predictions (AESOP): an entropically weighted information-theoretic measure for benchmarking computational accuracy. In Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2015, Atlanta, GA, USA, September 9-12, 2015. pages 495-496, ACM, 2015. [doi]

Abstract

Abstract is missing.