A Perceptron Classifier, Its Correctness Proof and a Probabilistic Interpretation

In this paper a fault tolerant probabilistic kernel version with smoothing parameter of Minsky’s perceptron classifier for more than two classes is exhibited and a correctness proof is provided. Moreover it is shown that the resulting classifier approaches optimality. Due to the non-determinism of the algorithm the (approximately) optimal value of a smoothing parameter has to be determined experimentally. The resulting complexity nevertheless allows for an efficient implementation employing for example Java concurrent programming and suitable hardware. In addition a probabilistic interpretation using Bayes Theorem is provided.

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