1998 InductiveLearningAlgsForTextCat

• (Dumais et al., 1998) ⇒ Susan T. Dumais, John C. Platt, David Heckerman, and Mehran Sahami. (1998). “Inductive Learning Algorithms and Representations for Text Categorization.” In: [[journal::Proceedings of the Seventh International Conference on Information and Knowledge Management] (CIKM 1998).

Subject Headings: Text Categorization Algorithm, Text Categorization Task.

Cited By

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Quotes

Author Keywords

Text categorization, classification, support vector machines, machine learning, information management.

Abstract =

Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size , and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate.,

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1998 InductiveLearningAlgsForTextCat	David Heckerman Susan T. Dumais John C. Platt Mehran Sahami			Inductive Learning Algorithms and Representations for Text Categorization			http://dx.doi.org/10.1145/288627.288651	10.1145/288627.288651		1998