Learning with Permutably Homogeneous Multiple-Valued Multiple-Threshold Perceptrons

摘要

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.