System and method for learning a network of categories using...

Data processing: artificial intelligence – Machine learning

Reexamination Certificate

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Reexamination Certificate

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07877335

ABSTRACT:
An improved system and method is provided for efficiently learning a network of categories using prediction. A learning engine may receive a stream of characters and incrementally segment the stream of characters beginning with individual characters into larger and larger categories. To do so, a prediction engine may be provided for predicting a target category from the stream of characters using one or more context categories. Upon predicting the target category, the edges of the network of categories may be updated. A category composer may also be provided for composing a new category from existing categories in the network of categories, and a new category composed may then be added to the network of categories. Advantageously, iterative episodes of prediction and learning of categories for large scale applications may result in hundreds of thousands of categories connected by millions of prediction edges.

REFERENCES:
patent: 7788248 (2010-08-01), Forstall et al.
patent: 2006/0179074 (2006-08-01), Martin et al.
patent: 2008/0243736 (2008-10-01), Rieman et al.
M.F. Caropreso, S. Matwin, and F. Sebastiani, “A Learner-Independent Evaluation of the Usefulness of Statistical Phrases for Automated Text Categorization”, Text Databases and Document Management: Theory and Practice, A.G. Chin, ed., Idea Group Publishing, Hershey, PA, pp. 78-102, 2001.
Fabrizio Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, vol. 34, No. 1, Mar. 2002, pp. 1-47.
Jianzeng Wang et al., “Using Fuzzy Cognitive Map to Effectively Classify E-Documents and Application”, GCC 2005, LNCS 3795, pp. 591-596, 2005.
Aguilar (Jose Aguilar, “A Survey about Fuzzy Cognitive Maps Papers”, Int'l Journal of Comp. Cognition vol. 3, No. 2, Jun. 2005) discusses FCMs.
Honkela et al. (Timo Honkela et al., “WEBSOME—Self-Organizing Maps of Document Collections”, Proceedings of WSOM, 1997, pp. 1-6) discuss SOMs as applied to document classification.
Lin et al. (Xia Lin, Dagobert Soergel, and Gary Marchionini, “A Self-organizing Semantic Map for Information Retrieval”, Research and Development in Information Research, Annual ACM Conference on, 1991, pp. 262-69) disclose a self-organizing semantic map.
Ritter et al. (H. Ritter and T. Kohonen, Self-Organizing Semantic Maps) discuss the title subject.
J. Bhogal, A. Macfarlane, and P. Smith “A review of ontology based query expansion”, Information Processing and Management 43 (2007) 866-86, available online Oct. 23, 2006.
J. T. Goodman, A Bit of Progress in Language Modeling, Computer Speech and Language, 15(4):403-434, Oct. 2001.
J. Z. Wang, J. Li, and G. Wiederhold, SIMPLIcity: Semantics-sensitive Integrated Matching for Picture Libraries, IEEE Transactions on Pattern Analysis and Machine Intelligence.
R. Rosenfeld, Two Decades of Statistical Language Modeling: Where Do We Go From Here, IEEE, 88(8), 2000.
Y. Even-Zohar and D. Roth, A Classification Approach to Word Prediction, In Annual meeting of the North American Association of Computational Linguistics (NAACL), 2000.

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