Inductive pattern learning

A general (nonheuristic) computational analytical model to tackle the difficult unsupervised inductive learning problem is proposed by making some additions and modifications to an existing metric model so that the model is more elegant and able to handle the unsupervised case. It turns out that it is instructive to treat, in essence, the supervised problem with noise as an unsupervised problem. We demonstrate the success of the new model on the benchmark XOR (exclusive-or) and parity problems by showing how the inductive agent successfully learns the weights in a dynamic manner that would allow it to distinguish between bit-strings of any length and unknown labels.