Abstract
This chapter is concerned with some machine learning algorithms which are used as the typical approaches to text categorization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beaver, M.: Introduction to Probability and Statistics. PWS-KENT Publishing Company (1991)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2000)
Jo, T.: Index optimization with KNN considering similarities among features. In: The Proceedings of 14th International Conference on Advances in Information and Knowledge Engineering, pp. 120–124 (2015)
Jo, T.: Keyword extraction by KNN considering feature similarities. In: The Proceedings of The 2nd International Conference on Advances in Big Data Analysis, pp. 64–68 (2015)
Jo, T.: AHC based clustering considering feature similarities. In: The Proceedings of 11th International Conference on Data Mining, pp. 67–70 (2015)
Jo, T.: KNN based word categorization considering feature similarities. In: The Proceedings of 17th International Conference on Artificial Intelligence, pp. 343–346 (2015)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Proceedings of European Conference on Machine Learning, pp. 137–142 (1998)
Joachims, T.: Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Kluwer Academic, Boston (2002)
Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification with string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)
Minsky, M., Papert, S.: Perceptrons. MIT Press, Cambridge (1969)
Mitchell, T.: Machine Learning. McGraw-Hill Companies, New York (1997)
Platt, J.: Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Microsoft Research (1998)
Rosenblatt, F.: The perceptron: a probabilistic model for information sotrage and organization in the brain. Psychol. Rev. 65, 385–408 (1958)
Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Exploration in Microstructure of Cognition, vol. 1. MIT Press, Cambridge (1986)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2001)
Wiener, E.D.: A neural network approach to topic spotting in text. The Master Thesis of University of Colorado (1995)
Winter, R., Widrow, B.: Madaline rule II: training algorithm for neural networks. In: Proceedings of IEEE 2nd International Conference on Neural Networks, pp. 401–408 (1988)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Jo, T. (2019). Text Categorization: Approaches. In: Text Mining. Studies in Big Data, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-91815-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-91815-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91814-3
Online ISBN: 978-3-319-91815-0
eBook Packages: EngineeringEngineering (R0)