Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks

Complexity of feedforward networks computing binary classification tasks is investigated. To deal with unmanageably large number of these tasks on domains of even moderate sizes, a probabilistic model characterizing relevance of the classification tasks is introduced. Approximate measures of sparsity of networks computing randomly chosen functions are studied in terms of variational norms tailored to dictionaries of computational units. Probabilistic lower bounds on these norms are derived using the Chernoff-Hoeffding Bound on sums of independent random variables, which need not be identically distributed. Consequences of the probabilistic results on the choice of dictionaries of computational units are discussed