Intelligent analysis is used in almost all areas of technology. Machine learning does not stand still and is constantly evolving. Given the transition in modern society to electronic document management, the main assumption in them is that the training and test data must be in the same feature space and follow the same distribution. In real applications, this is not always the case. In this case, the role of transfer learning can be distinguished since transfer learning does not make the same distributional assumptions as traditional machine learning and reduces dependencies on the target task and training data, and has a wider knowledge migration. The article proposes a transfer learning algorithm for document categorization based on clustering. An experiment is also used to test the algorithm. The experiment shows that the algorithm proposed in this article has its advantages.
Intelligent analysis is used in almost all areas of technology. Machine learning does not stand still and is constantly evolving. Given the transition in modern society to electronic document management, the main assumption in them is that the training and test data must be in the same feature space and follow the same distribution. In real applications, this is not always the case. In this case, the role of transfer learning can be distinguished since transfer learning does not make the same distributional assumptions as traditional machine learning and reduces dependencies on the target task and training data, and has a wider knowledge migration. The article proposes a transfer learning algorithm for document categorization based on clustering. An experiment is also used to test the algorithm. The experiment shows that the algorithm proposed in this article has its advantages.
Aqlli tahlil texnologiyaning deyarli barcha sohalarida qo'llaniladi. Mashinani o'rganish bir joyda turmaydi va doimo rivojlanib boradi. Zamonaviy jamiyatda elektron hujjat aylanishiga o'tishni hisobga olgan holda, ulardagi asosiy taxmin shundan iboratki, o'quv va test ma'lumotlari bir xil xususiyat maydonida bo'lishi va bir xil taqsimotga amal qilishi kerak. Haqiqiy dasturlarda bu har doim ham shunday emas. Bunday holda, transferni o'rganishning rolini ajratish mumkin, chunki transferni o'rganish an'anaviy mashinani o'rganish bilan bir xil taqsimot taxminlarini keltirib chiqarmaydi va maqsadli vazifa va o'quv ma'lumotlariga bog'liqlikni kamaytiradi va bilimlarning kengroq migratsiyasiga ega. Maqolada klasterlash asosida hujjatlarni tasniflash uchun transferni o'rganish algoritmi taklif etiladi. Algoritmni sinash uchun tajriba ham qo'llaniladi. Tajriba shuni ko'rsatadiki, ushbu maqolada taklif qilingan algoritm o'zining afzalliklariga ega.
Интеллектуальный анализ используется практически во всех областях техники. Машинное обучение не стоит на месте и постоянно развивается. Учитывая переход современного общества к электронному документообороту, основное предположение в них заключается в том, что обучающие и тестовые данные должны находиться в одном и том же функциональном пространстве и следовать одинаковому распределению. В реальных приложениях это не всегда так. В этом случае можно выделить роль трансферного обучения, поскольку трансферное обучение не использует те же предположения о распределении, что и традиционное машинное обучение, и уменьшает зависимость от целевой задачи и обучающих данных, а также обеспечивает более широкую миграцию знаний. В статье предлагается алгоритм обучения передаче для категоризации документов на основе кластеризации. Для проверки алгоритма также используется эксперимент. Эксперимент показывает, что алгоритм, предложенный в этой статье, имеет свои преимущества.
| № | Имя автора | Должность | Наименование организации |
|---|---|---|---|
| 1 | Abduvaileva Z.A. | Student | TUIT |
| 2 | Ergashev S.B. | Assistant | 1Jizzakh branch of the National University of Uzbekistan |
| № | Название ссылки |
|---|---|
| 1 | 1. Marszalek M., Schmid S., Harzalla H., Van de Weyer J. Learning object representations for class recognition of visual objects. In: Seminar on ICCV Visual Recognition Problems. 2007. [1] 2. Mikhalkova L., Mooney R.J. Transfer learning by matching against minimum target data. In: Proc. assoc. to advance the workshop on artificial intelligence transfer learning for complex tasks. 2008 [2] 0 100 200 300 400 500 600 Students' documents Plan Scientific works Teachers documents Departments document Results after clustering before clustering “Raqamli iqtisodiyot” ilmiy-elektron jurnali | №1-son | 2022 97 3. Moreno O., Shapira B., Rokach L., Shani G. (2012) TALMUD—transfer learning for several areas. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. [3] 4. Nam J., Kim S. (2015) Prediction of heterogeneous defects. In: Proceedings of the 10th 2015 Joint Meeting on Fundamentals of Software Engineering. 2015. [4] 5. Ng MK, Wu Q, Ye Y. Collaborative learning with a method based on a joint transition probability graph. In: Proceedings of the 1st International Workshop on Revealing Cross-Domain Knowledge in Web and Social Networks. 2012. [5] 6. W. Dai, Q. Yang, G. Xue and Y.Yu, “Boosting for Transfer learning”, Proc.24 International Conference. Machine Learning, pp.193-200, June 2007. [6] 7. Xinno Jialing Pan, Yang K. Survey on transfer learning. IEEE TKDE, 2009. [7] 8. Dai V, Yang Q, Xue GR, Yu Yu. Boosting for transfer learning. Proceedings of the Twenty-fourth International Conference on Machine Learning, 2007: 193 × 200. [8] 9. Dai Wee, Chen WK, Xue GR, Yang Q, Yu Yu. Transfer learning: Transferring learning to different functional spaces. Advances in Neural Information Processing Systems 21, 2009. [9] |