Machine Learning Techniques in Spam Filtering – Ing. Aliaksandr Barushka
Ing. Aliaksandr Barushka
Doctoral thesis
Machine Learning Techniques in Spam Filtering
Machine Learning Techniques in Spam Filtering
Abstract:
The rapid growth of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Machine-learning methods such as Naive Bayes, support vector machines or neural networks have been particularly effective in categorizing spam/non-spam messages. In order to further enhance the performance of review spam detection, I propose a novel contentbased approach that considers both …moreAbstract:
The rapid growth of unsolicited and unwanted messages has inspired the development of many anti-spam methods. Machine-learning methods such as Naive Bayes, support vector machines or neural networks have been particularly effective in categorizing spam/non-spam messages. In order to further enhance the performance of review spam detection, I propose a novel contentbased approach that considers both …more
Language used: English
Date on which the thesis was submitted / produced: 31. 3. 2020
Accessible from:: 31. 12. 2999
Thesis defence
- Date of defence: 2. 6. 2020
- Supervisor: doc. Ing. Petr Hájek, Ph.D.
Citation record
ISO 690-compliant citation record:
BARUSHKA, Aliaksandr. \textit{Machine Learning Techniques in Spam Filtering}. Online. Doctoral theses, Dissertations. Pardubice: University of Pardubice, Faculty of Economics and Administration. 2020. Available from: https://theses.cz/id/nss20e/.
The right form of listing the thesis as a source quoted
Barushka, Aliaksandr. Machine Learning Techniques in Spam Filtering. Pardubice, 2020. disertační práce (Ph.D.). Univerzita Pardubice. Fakulta ekonomicko-správní
Full text of thesis
Accessibility: Autor si nepřeje zpřístupnění práce veřejnosti
Contents of on-line thesis archive
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Other ways of accessing the text
Institution archiving the thesis and making it accessible: Univerzita Pardubice, Fakulta ekonomicko-správníUniversity of Pardubice
Faculty of Economics and AdministrationDoctoral programme / field:
Applied Informatics / Applied Informatics
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