Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2011-07-26
2011-07-26
Starks, Jr., Wilbert L (Department: 2129)
Data processing: artificial intelligence
Neural network
Learning task
C706S045000
Reexamination Certificate
active
07987144
ABSTRACT:
A data classification method and apparatus are disclosed for labeling unknown objects. The disclosed data classification system employs a learning algorithm that adapts through experience. The present invention classifies objects in domain datasets using data classification models having a corresponding bias and evaluates the performance of the data classification. The performance values for each domain dataset and corresponding model bias are processed to identify or modify one or more rules of experience. The rules of experience are subsequently used to generate a model for data classification. Each rule of experience specifies one or more characteristics for a domain dataset and a corresponding bias that should be utilized for a data classification model if the rule is satisfied. The present invention dynamically modifies the assumptions (bias) of the learning algorithm to improve the assumptions embodied in the generated models and thereby improve the quality of the data classification and regression systems that employ such models. The disclosed self-adaptive learning process will become increasingly more accurate as the rules of experience are accumulated over time.
REFERENCES:
McAulay, A.D.; Oh, J.C.; Improved learning in genetic rule-based classifier systems, Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on, Oct. 13-16, 1991, p. 1393.
Lewis, David D., An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task, Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jun. 1992, pp. 37-50.
Lesh, N., Scalable feature mining for sequential data, Intelligent Systems, IEEE [see also IEEE Expert], vol. 15 Issue: 2 , Mar.-Apr. 2000, pp. 48-56.
Fogel, D. B., Evolutionary programming for training neural networks, Neural Networks, 1990., 1990 IJCNN International Joint Conference on, Jun. 17-21, 1990, pp. 601-605 vol. 1.
Santos, R., Biased clustering methods for image classification, Computer Graphics, Image Processing, and Vision, 1998. Proceedings. SIBGRAPI '98. International Symposium on, Oct. 20-23, 1998, pp. 278-285.
Liu, Yan et al, Classification: Boosting to correct inductive bias in text classification, Proceedings of the eleventh international conference on Information and knowledge management, Nov. 2002, pp. 348-355.
Bernhard E. Boser, Isabelle M. Guyon, Vladimir N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory, Jul. 1992, pp. 144-152.
McAulay, A.D.; Oh, J.C.; Improved leaming in genetic rule-based classifier systems, Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on, Oct. 13-16, 1991, pp. 1393-1398 vol. 2.
Lewis, David D., An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task, Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jun. 1992, pp. 37-50.
The Authoritative Dictionary of IEEE Standards Terms, Seventh Edition, The Institute of Electrical and Electronics Engineering, Inc., 3 Park Avenue, New York, NY, 10016-5997, USA, 2000, p. 688.
Raynor, W., The International Dictionary of Artificial Intelligence, The Glenlake Publishing Company, Ltd., Chicago, IL 60660, 1999, p. 186.
McAulay, et al., Improved learning in genetic rule-based classifier systems, Systems, Man, and Cybernetics, 1991, Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on, vol. 2 Oct. 13-16, 1991, pp. 1393-1398.
Breiman., “Bagging Predictors,” Machine Learning, 23, 123-140 (1996).
Gama et al., “Characterization of Classification Algorithms,” In C Pinto-Ferreira and N. Mamede (eds.) Progress in Artificial Intelligence, 189-200 (1995).
Perez et al., “Learning Depsite Concept Variation by Finding Structure in Attribute-Based Data,” International Conference on Machine Learning (1996).
Thrun et al., “Learning One More Thing,” Proc. of ISCA Montreal (1995).
Drissi Youssef
Vilalta Ricardo
International Business Machines - Corporation
Ryan & Mason & Lewis, LLP
Starks, Jr. Wilbert L
LandOfFree
Methods and apparatus for generating a data classification... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Methods and apparatus for generating a data classification..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Methods and apparatus for generating a data classification... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2716534