Identyfikatory
Warianty tytułu
Liniowa sieć SVM w zastosowaniu do organizacji danych
Konferencja
PELINCEC Workshop "Bridges Through Time: Intelligent Control, Signal Processing and Real-Time Process Control"
Języki publikacji
Abstrakty
This paper demonstrates that the text categorization (TC) is a good automatic method for organizing data. Some features of the TC problem are described and explained that linear Support Vector Machines (SVM) is an appropriate technique for this task. Theoretical considerations are illustrated through examples in which the text categorization problem has been solved with SVM.
Artykuł omawia problem kategoryzacji tekstu jako dobrego rozwiązania do automatycznej organizacji danych. Przedstawia cechy problemu TC oraz wyjaśnia, iż liniowa metoda wektorów podtrzymujących (SVM) doskonale sprawdza się w tego typu zadaniach. Teoretyczne rozważania ilustrowane są przykładami automatycznej organizacji dokumentów przy wykorzystaniu sieci typu SVM.
Wydawca
Czasopismo
Rocznik
Tom
Strony
47--49
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
- Warsaw University of Technology, jurczakm@isep.pw.edu.pl
Bibliografia
- [1] Drucker H., Wu D., Vapnik V., Support Vector Machines for spam categorization, IEEE Trans Neural Networks, 10 (1999), 1048-1054
- [2] Lewis D., Ringuette M., A comparison of two learning algorithms for text classification, Third Annual Symposium on Document Analysis and Information Retrieval, (1994), 81-93
- [3] Yang Y., Pedersen J., Feature selection in statistical learning of text categorization, Int. Conf. on Machine Learning, (1998) 412-420
- [4] Wiener E., Pedersen J, Weigend A., A neural network approach to topic spotting, Proc. 4th annual symposium on document analysis and information retrieval, (1993), 22-34
- [5] Joachims T., Text categorisation with Support Vector Machines: Learning with many relevant features, Proc. Of the European Conference on Machine Learning (ECML), Springer, (1998), 137-142
- [6] Joachims T., Learning to classify text using support vector machines, Kluwer Academic Publishers, (2002)
- [7] Porter M., An algorithm for suffix stripping, Program (Automated Library and Information Systems),14 (1980), 130-137
- [8] Berry M., Dumais S., Shippy A., A case study of latent semantic indexing, University of Tennesse at Knoxville, Tech Rep., nrCS-95-271 (1995)
- [9] Sebastini F., A tutorial on automated text categorization, Proc of ASAI-99, 1st Argentinean Symposium on Al, Buenos Aires, (1999), 7-35
- [10] Gunn S., Support Vector Machines for classification and regression, University of Southampton, Tech. Rep., (1998)
- [11] Cortes C., Vapnik V., Support vector networks, Machine Learning Journal, 20 (1995), 273-297
- [12] Vapnik V., The nature of statistical learning theory, New York, Springer-Verlag, (1995)
- [13] Burges C., A tutorial on support Victor machines for pattern recognition, Knowledge Discovery and Data Mining, 2 (1998)
- [14] Cristianini N., Shawe-Taylor J., An Introduction to Support Vector Machines, Cambridge University Press, UK, (2000)
- [15] Joachims T., Making large-scale SVM learning practical, MIT-Press, (1999)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0014-0029