Learning with permutably homogeneous multiple-valued multiple-threshold perceptrons

The (n, k, s)-perceptrons partition the input space V ⊃ R ⁿ into s + 1 regions using s parallel hyperplanes. Their learning abilities are examined in this research paper. The previously studied homogeneous (n, k, k - 1)-perceptron learning algorithm is generalized to the permutably homogeneous (n, k, s)-perceptron learning algorithm with guaranteed convergence property. We also introduce a high capacity learning method that learns any permutably homogeneously separable k-valued function given as input.