????? ???????? ???????????? ?? ??????? ??????? ?? ????????????
Електронний архів Житомирського державного технологічного університету
Переглянути архів Інформація| Поле | Співвідношення | |
| Title |
????? ???????? ???????????? ?? ??????? ??????? ?? ????????????
Method of neural network model w?th real output construct?onon precedents |
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| Creator |
????????, ?.?.
Subbot?n, S.A. |
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| Subject |
???????? ??????
?????? ??????? ??????? ???????? ?????????????? neural networks feature selection machine learning diagnosis |
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| Description |
???????? ???????? ????????????? ??????? ??????? ?????????? ??????????? ?? ????????????. ????????????? ????? ???????? ??????? ?i???i???? ??????????? ?? ???????????? ?? ?????? ????????? ?????, ?? ?????????? ???????i??i ?? ??????????? ??????????? ?????. ??????????? ?? ????????? ????????????? ?????? ???????? ?????? ??? ?????? ???????????: ?????? ???? ???????? ?????????? ?????????????? ?????????? ?? ???????? ? ???????? ?????, ??????? ???????? ?????????? ?? ?????????? ???????????? ???????? ??? ?????????, ?????? ???????? ????? ????????? ?????? ?????????? ?? ?????????? ?????????????? ?????????? ?? ??????? ????????. ???????????? ????????????? ?????? ???????? ???????? ???????????????? ??????????? ? ??????????? ?????????? ?? ???????? ?????????????? ???????????. ????????? ???????????????? ??????????? ???????????? ?????? ??? ????????? ?????????? ???????. The problem of automation of quantitative dependence model synthesis on precedents is solved. The method of quantitative dependence model construction on pecedents based on neural networks taking into account the topological and statistical properties of the data is proposed. The neural network synthesized using the proposed method has block architecture: the first block defines belonging of recognized instance to the cluster in the feature space, a second block realize calculations for partial regression models for clusters, the third block selects partial model according to recognized instance belonging to a particular cluster. The using of the proposed method provides to receive the interpretable neural network models with minimal complexity and high generalization abilities. The experimental study of the developed method in solving practical problems is conducted. |
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| Date |
2016-03-31T10:47:00Z
2016-03-31T10:47:00Z 2013 |
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| Type |
Article
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| Identifier |
http://eztuir.ztu.edu.ua/123456789/2451
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| Language |
uk
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| Relation |
?????? ????. ?????: ???????? ?????;4(67)
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| Publisher |
????
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