Incremental training for probabilistic categorizer

Details Incremental training for probabilistic categorizer Incremental training for probabilistic categorizer

2005-06-29

2010-12-07

Pannala, Sathyanarayan (Department: 2164)

Data processing: database and file management or data structures

Database and file access

Preparing data for information retrieval

C704S009000

Reexamination Certificate

active

07849087

ABSTRACT:
A probabilistic document categorizer has an associated vocabulary of words and an associated plurality of probabilistic categorizer parameters derived from a collection of documents. A new document is received. The probabilistic categorizer parameters are updated to reflect addition of the new document to the collection of documents based on vocabulary words contained in the new document, a category of the new document, and a collection size parameter indicative of an effective total number of instances of vocabulary words in the collection of documents.

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Yang et al., “A Comparative Study on Feature Selection in Text Categorization,” InProceedings of ICML-97, 14thInternational Conference on Machine Learning, pp. 412-420, 1997.
Gaussier et al., “A Hierarchical Model for Clustering and Categorising Documents,” InAdvances in Information Retrieval—Proceedings of the 24thBCS-IRSG European Colloquium on IR Research, vol. 2291 ofLecture Notes in Computer Science, pp. 229-247. Springer, 2002.
U.S. Appl. No. 10/774,966, filed Feb. 9, 2004, Goutte et al.

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