Abstract We geometrically classify multi-layer perceptron (MLP) solutions in two ways: the hyperplane partitioning interpretation and the hidden-unit representation of the pattern set. We show these classifications to be invariant under orthogonal transformations and translations in the space of the hidden units. These solitots [sic] can be enumerated for any given Boolean mapping problem. Using a geometrical argument we derive the total number of solitots available to a minimal network for the parity problem. A lower bound is computed for the scaling of the number of solitots with input vector dimension, when a fixed fraction of patterns is removed from the full training set. The generalization probability is shown to decrease with the exponential of the problem size for the parity problem. We suggest that this, and hidden layer scaling problems, are serious drawbacks to scapling-up of MLPs to larger tasks.
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