A Computational Study on Circuit Size vs. Circuit Depth

Abstract

We investigate the circuit complexity of classification problems in a machine learning setting, i.e. we attempt to find some rule that allows us to calculate a priori the number of threshold gates that is sufficient to achieve a small error rate after training a circuit on sample data ${\mathcal S}_L$. The particular threshold gates are computed by a combination of the classical perceptron algorithm with a specific type of stochastic local search. The circuit complexity is analysed for depth-two and depth-four threshold circuits, where we introduce a novel approach to compute depth-four circuits. For the problems from the UCI Machine Learning Repository we selected and investigated, we obtain approximately the same size of depth-two and depth-four circuits for the best classification rates on test samples, where the rates differ only marginally for the two types of circuits. Based on classical results from threshold circuit theory and our experimental observations on problems that are not linearly separable, we suggest an upper bound of $8\cdot \sqrt{2^n/n}$ threshold gates as sufficient for a small error rate, where $n := \log|{\mathcal S}_L|$.