|
Library
|
Your profile |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|
Software systems and computational methods
Reference:
Korobeynikov A.G., Sidorkina I.G., Blinov S.Yu., Leyman A.V.
Algorithm of information classification
for solving the problem of spam filtration.
// Software systems and computational methods.
2012. ¹ 1.
P. 80-88.
URL: https://en.nbpublish.com/library_read_article.php?id=61565
Korobeynikov A.G., Sidorkina I.G., Blinov S.Yu., Leyman A.V. Algorithm of information classification for solving the problem of spam filtration.Abstract: In this article authors consider the problem of spam determination and filtering based on the support vector machine. A modi fication of construction of a separating hyper plane using Fejer mappings is given. Authors suggest replacing the projection operation with the sequence of mapping operations to allow the work with the time-varying data, specific to the problems of classification of documents. Keywords: Software, classification of informa tion, spam, strong separability problem, Hil bert space, support vector method, the hyperplane, Fejér mapping algorithm conver gence, filtering
This article can be downloaded freely in PDF format for reading. Download article
References
1. Cidorkina I.G., Korobeynikov A.G, Kudrin P.A. Algoritm raspoznavaniya trekhmernykh izobrazheniy s vysokoy detalizatsiey// Vestnik MarGTU, 2 (9), 2010 g., str. 91-99.
2. Grishentsev A.Yu., Korobeynikov A.G. Dekompozitsiya N-mernykh tsifrovykh signalov po bazisu pryamougol'nykh vspleskov//Nauchno-tekhnicheskiy vestnik informatsionnykh tekhnologiy, mekhaniki i optiki. – 2012. – ¹ 4 (80). – S. 75–79. 3. Khalmosh P.., Gil'bertovo prostranstvo v zadachakh, Perevod s angliyskogo I. D. Novikova i T. V. Sokolovskoy; pod red. R. A. Minlosa. — M.: Izdatel'stvo «Mir», 1970. — 352 s. 4. Eremin I.I. Feyerovskie metody sil'noy otdelimosti vypuklykh poliedral'nykh mnozhestv // Izvestiya vuzov. Ser. matematika.-2006.-¹ 12.-C. 33-43. 5. Lifshits Yu. Klassifikatsiya tekstov. Algoritmy dlya Interneta. 2005. URL: http://yury.name/internet. 6. R.-E. Fan, P.-H. Chen, C.-J. Lin. Working set selection using second order information for training SVM // Journal of Machine Learning Research, V. 6, 2005, pp. 1889–1918. 7. Eremin I.I., Mazurov V.D. Nestatsionarnye protsessy matematicheskogo programmirovaniya.-M.: Nauka, 1979.-288 s. 8. Eremin I.I. Teoriya lineynoy optimizatsii.-Ekaterinburg: UrO RAN, 1999.-312 s. 9. Nurminskiy E.A. Ispol'zovanie dopolnitel'nykh malykh vozdeystviy v feyerovskikh modelyakh iterativnykh algoritmov//Zh. vychisl. matem. i matem. fiz. 2008. T. 48. ¹ 12. S. 2121–2128. 10. Zhuravlev Yu. I. Ob algebraicheskom podkhode k resheniyu zadach raspoznavaniya ili klassifikatsii // Problemy kibernetiki. – 1978. – T. 33. – S. 5–68. http://www.ccas.ru/frc/papers/zhuravlev78prob33.pdf |
© 1998 – 2024 Nota Bene. Publishing Technologies. NB-Media Ltd.
