Feature reduction method of decision machines

Details Feature reduction method of decision machines Feature reduction method of decision machines

2005-12-14

2010-11-09

Holmes, Michael (Department: 2129)

Data processing: artificial intelligence

Machine learning

Reexamination Certificate

active

07831527

ABSTRACT:
A method for feature reduction in a training set for a learning machine such as a Support Vector Machine (SVM). In one embodiment the method includes a step (35) of receiving input training data vectors xiof a training set. The input training data vectors are typically derived from a set of features in a feature space. At step (37) the input data vectors are mapped into a multi-dimensional space. At step (39) a least squares problem, derived from a formulation of the SVM, is solved to determine which features comprising the training vectors are to be deemed significant. At step (41) decision parameters and vectors of the chosen decision machine, e.g. SVM, are determined using the features determined to be significant in step (39).

REFERENCES:
patent: 7079955 (2006-07-01), Oron et al.
patent: 7356187 (2008-04-01), Shanahan et al.
patent: 7398191 (2008-07-01), Gluhovsky et al.
patent: 7401012 (2008-07-01), Bonebakker et al.
patent: 7406450 (2008-07-01), Graf et al.
patent: 7415359 (2008-08-01), Hill et al.
patent: 7478074 (2009-01-01), Gates
patent: 2004/0015462 (2004-01-01), Lienhart et al.
Learning automatic acquisition of subcategorization frames using Bayesian inference and support vector machines; Maragoudakis, M.; Kermanidis, K.; Fakotakis, N.; Kokkinakis, G.; Data Mining, 2001. ICDM, Proceedings IEEE International Conference on Digital Object Identifier: 10.1109/ICDM.2001.989583 Publication Year: 2001 , pp. 623-625.
Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge; Krapivin, M.; Marchese, M.; Yadrantsau, A.; Yanchun Liang; Digital Information Management, 2008. ICDIM 2008. Third International Conference on Digital Object Identifier: 10.1109/ICDIM.2008.4746749 Publication Year: 2008 , pp. 105-112.
Learning automatic acquisition of subcategorization frames using Bayesian inference and support vector machines, Maragoudakis, M.; Kermanidis, K.; Fakotakis, N.; Kokkinakis, G.; Data Mining, 2001. ICDM, Proceedings IEEE International Conference on Digital Object Identifier: 10.1109/ICDM.2001.989583 Pub Year: 2001 , pp. 623-625.
Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture, Schuller, B.; Rigoll, G.; Lang, M.; Acoustics, Speech, and Signal Processing, Proceedings. (ICASSP '04). IEEE Intl Conference on vol. 1 Digital Object Identifier: 10.1109/ICASSP.2004 pp. 577-580.
One-sided Sampling for Learning Taxonomic Relations in the Modern Greek Economic Domain, Kermanidis, K.; Fakotakis, N.; Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on vol. 2 Digital Object Identifier: 10.1109/ICTAI.2007.72 Publication Year: 2007 , pp. 354-361.
Study on Prediction of Prosodic Phrase Boundaries in Chinese TTS, Ziping Zhao; Yaoting Zhu; Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on vol. 3 Digital Object Identifier: 10.1109/SNPD.2007.67 Publication Year: 2007 , pp. 354-361.
Lee, Y.J. and Mangasarian, O.L. 'RSVM: Reduced Support Vector Machine′, Computer Sciences Dept., Univ. of Wisconsin, Madison, Wisconsin, Jul. 2000.
International Search Report for PCT/AU2005/001888, Date of Mailing: Feb. 6, 2006, 1 page.
Supplementary European Search Report and European Search Opinion for EP 05815793.4 (PCT/AU2005/001888), Date of Mailing: Dec. 12, 2009, 9 pages.
Avidan, Shai, “Joint feature-basis subset selection,” Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), 1063-6919/04, 2004©, 8 pages.
Chen, Jihong, “Sparse Modeling in Classification, Compression and Detection,” A Thesis Presented to The Academic Faculty, School of Industrial and Systems Engineering Georgia Institute of Technology, Jul. 2004, 121 pages.
Gates, K. E., “Fast and Accurate Face Recognition Using Support Vector Machines,” Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005©, 5 pages.
Masters, Annette Louise, “Some extensions to support vector machines,” http://library.uq.edu.au/record=b216056-S7, St. Lucia, Qld. 2003, 3 pages.
Schölkopf, Bernhard, et al., “Input Space Versus Feature Space in Kernel-Based Methods,” IEEE Transactions on Neural Networks, 10(5), Sep. 1999, 18 pages.
Valyon, József, et al., “A Weighted Generalized LS-SVM,” Periodica Polytechnica Ser. El Eng., 47(3-4), pp. 229-251, (2003).
Zhu, Yongsheng et al., “A new type SVM—projected SVM,” Science in China Ser. G Physics, Mechanics & Astronomy, 2004, vol. 47 Supp. 21-28.

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