Large margin perceptrons for document categorization

Details Large margin perceptrons for document categorization Large margin perceptrons for document categorization

2006-08-22

2006-08-22

Knight, Anthony (Department: 2121)

Data processing: artificial intelligence

Neural network

Learning task

C706S020000, C706S025000

Reexamination Certificate

active

07096208

ABSTRACT:
A modified large margin perceptron learning algorithm (LMPLA) uses asymmetric margin variables for relevant training documents (i.e., referred to as “positive examples”) and non-relevant training documents (i.e., referred to as “negative examples”) to accommodate biased training sets. In addition, positive examples are initialized to force at least one update to the initial weighting vector. A noise parameter is also introduced to force convergence of the algorithm.

REFERENCES:
John C. Platt, Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines, 1998, Microsoft Research.
Smola et al., Advances in Large Margin Classifiers, 2000, The MIT Press, pp. 21, 312, 315.
Brown et al., Support Vector Machine Classification of Microarray Gene Expression Data, 1999, UCSC-CRL-99-09.
Cortes, C., Vapnik, V.; “Support-Vector Networks”; Machine Learning, 20, 1995 pp. 273-297.
Dumais, S., Platt, J., Heckerman, D., Mehran, S.; “Inductive Learning Algorithms and Representations for Text Categorization”; Proceedings of the seventh International Conference on Information Knowledge Management 1998, pp. 148-155.
Joachims, T.; “Text Categorization with Support Vector Machines: Learning with Many Relevant Features” Technical Resport, University Dortmund, Dept of Artificial Intelligence, 1997, 18 pgs.
Joachims, T.; “Making Large-Scale SVM Learning Practical” Advances in Kernel Methods—Support Vector Learning, MIT Press, 1998, 17 pgs.
Littlestone, N., Warmuth, M.; “Relating Data Compression and Learnability” University of California at Santa Cruz, Jun. 1986, pp. 1-13.
Platt, J.; “Fast Training of Support Vector Machines using Sequential Minimal Optimization”; Advances in Kernel Methods—Support Vector Learning, MIT Press, 1998, pp. 185-208.
Shawe-Taylor, J., Barlett, P., Williamson, R.C., Anthony, M.; “Structural Risk Minimization over Data-Dependent Hierarchies”; IEEE Transactions on Information Theory, 44, 1998, pp. 1-33.
Rosenblatt, F.; “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”; Psychological Review, 65, 1958, pp. 92-114.
Herbrich, R.; Learing Kernel Classifiers: Theory and Algorithms MIT Press 2001, relevant excerpts pp. 8-11, 17-27, 323-324, 334-340.

Affiliated with

Also associated with

Rating

Large margin perceptrons for document categorization does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Large margin perceptrons for document categorization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Large margin perceptrons for document categorization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3712293