Information theoretic upper bounds on the number of distinguishable classes

This paper examines data driven information theoretic upper bounds on the number of classes that can be distinguished by machine-learning classification systems as a function of the signal-to-noise ratio (SNR) of the features. Fano upper bounds are derived with desired classification error as a parameter. A simulation example is used to explore the bounds.