Abstract
In this paper a fault tolerant probabilistic kernel version with smoothing parameter of Minsky’s perceptron classifier for more than two classes is sketched. Moreover a probabilistic interpretation of the output is exhibited. The price one has to pay for this improvement appears in the non-determinism of the algorithm. Nevertheless an efficient implementation using for example Java concurrent programming and suitable hardware is shown to be possible. Encouraging preliminary experimental results are presented.
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Acknowledgments
The author is indebted to M. Stern for help with some problems concerning the Java system.
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Falkowski, BJ. (2017). A Perceptron Classifier and Corresponding Probabilities. In: Ferraro, M., et al. Soft Methods for Data Science. SMPS 2016. Advances in Intelligent Systems and Computing, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-42972-4_27
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DOI: https://doi.org/10.1007/978-3-319-42972-4_27
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