Abstract
The generating of training datasets for machine learning projects is a topical problem. The cost of dataset formation can be considerably high, yet there is no guarantee of an acceptable quality of prepared data. The important issue of dataset generation is the labeling noise. The main causes of this phenomena are: expert errors, information insufficiency, subjective factors and so on. Labeling noise affects the learning stage of a neuronet and so increases the number of errors during the one’s functioning. In the current paper the technique to decrease the labeling noise level is proposed. It is based on the principals of the distributed ledger technology. While there is a possibility to decrease the labeling errors number, the services integration on the basis of distributed ledger allows to improve the efficiency of dataset forming.
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The paper has been prepared within the RFBR project 18-29-22086 and 18-29-22046.
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Melnik, E.V., Klimenko, A.B., Ivanov, D.Y. (2019). The Distributed Ledger-Based Technique of the Neuronet Training Set Forming. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Computational Statistics and Mathematical Modeling Methods in Intelligent Systems. CoMeSySo 2019 2019. Advances in Intelligent Systems and Computing, vol 1047. Springer, Cham. https://doi.org/10.1007/978-3-030-31362-3_2
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