A regularization method for non-trivial codes in polychotomous classification

摘要

Polychotomous classification is a widespread task in pattern recognition. A classifier relates an input pattern to a class C_k element of a fixed number K>2 of classes C_1, C-2..., C_K. Neural networks for classification are generally based on the trivial 1- Out-of-K coding. The advantage of non-trivial codes for discrimination of multiple classes Lies in the increased Hamming distance between reference vectors, which makes error Detection and even error correction feasible.