In this paper, we describe a new probabilistic algorithm for calculating hypotheses as the results of similarities between training examples for a machine learning problem based on a binary similarity operation. Unlike previously proposed probabilistic algorithms, the order of accounting for training examples is fixed for all hypotheses. This algorithm is useful for implementation using a GPGPU. The main result of this paper is the independence of the order of the appearance of training examples of the probabilities of each similarity in the sample.
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The author thanks his colleagues from the Informatics and Management FRC, Russian Academy of Sciences for constructive criticism and useful comments.
This work was partially supported by the Russian Foundation for Basic Research, project no. 18-29-03063mk.
The authors declare that they have no conflicts of interest.
Translated by A. Ivanov
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Vinogradov, D.V. A Probabilistic Algorithm for Calculating Similarities. Autom. Doc. Math. Linguist. 53, 234–236 (2019) doi:10.3103/S0005105519050042
- probabilistic algorithm