Method and apparatus for improved regression modeling

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

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Reexamination Certificate

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07599898

ABSTRACT:
The present invention is a method and an apparatus for improved regression modeling to address the curse of dimensionality, for example for use in data analysis tasks. In one embodiment, a method for analyzing data includes receiving a set of exemplars, where at least two of the exemplars include an input pattern (i.e., a point in an input space) and at least one of the exemplars includes a target value associated with the input pattern. A function approximator and a distance metric are then initialized, where the distance metric computes a distance between points in the input space, and the distance metric is adjusted such that an accuracy measure of the function approximator on the set of exemplars is improved.

REFERENCES:
patent: 5754681 (1998-05-01), Watanabe et al.
patent: 6295509 (2001-09-01), Driskell
patent: 6353814 (2002-03-01), Weng
patent: 6939670 (2005-09-01), Pressman et al.
patent: 7020631 (2006-03-01), Freeman et al.
patent: 7165037 (2007-01-01), Lazarus et al.
patent: 7214485 (2007-05-01), Belinsky et al.
patent: 7260551 (2007-08-01), Phillips
patent: 7263492 (2007-08-01), Suresh et al.
patent: 7263506 (2007-08-01), Lee et al.
patent: 7363308 (2008-04-01), Dillon et al.
patent: 7373256 (2008-05-01), Nicholson et al.
patent: 7376618 (2008-05-01), Anderson et al.
patent: 7436511 (2008-10-01), Ruchti et al.
patent: 7451065 (2008-11-01), Pednault et al.
patent: 7512508 (2009-03-01), Rajski et al.
A regression approach to equivalent bandwidth characterization in ATM networks Ramaswamy, S.; Ono-Tesfaye, T.; Gburzyuski, P.; Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, 1997. MASCOTS '97., Proceedings Fifth International Symposium on Jan. 12-15, 1997 pp. 104-109 Digital Object Identifier 10.1109/MASCOT.1997.
Combining Landsat ETM+ and terrain data for scaling up leaf area index (LAI) in eastern Amazon: an intercomparison with MODIS product Shimabukuro, Y.E.; Aragao, L.E.O.C.; Espirito-Santo, F.D.B.; Williams, M.; Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International vol. 3, Sep. 20-24, 2004 pp. 2050-2053.
An intelligent system for financial time series prediction combining dynamical systems theory, fractal theory, and statistical methods Castillo, O.; Melin, P.; Computational Intelligence for Financial Engineering, 1995.,Proceedings of the IEEE/IAFE 1995 Apr. 9-11, 1995 pp. 151-155 Digital Object Identifier 10.1109/CIFER.1995.495269.
International Search Report and Written Opinion for PCT/US2007/078377, copy consists of 16 unnumbered pages.
Jacob Goldberger, et al., “Neighbourhood Compenents Analysis”, Internet Citation, [Online] 2004, XP007903888, Retrieved from the internet: URL:http://www.cs.toronto.edu/{hinton/absps
ca.pdf> [retrieved on Jan. 21, 2008].
Aha D W, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms”, International Journal of Man—Machine Studies, Academic Press, London, GB, vol. 36, 1992, pp. 267-287, XP007903847 ISSN: 0020-7373, Section 4: “Tolerating attributes with uncertain tolerance”, in particular, subsection 4.2: “IB4: Learning attribute relevance”.
Fukunaga, Keinosuke, “Introduction to Statistical Pattern Recognition, Chapter 6 (p. 254-299)”, 1990, Academic Press, Inc., Boston 207370, XP002466163.
Weinberger, Kilian Q., Tesauro, Gerald, “Metric Learning for Kernel Regression”, Proceedings of the 11thInternational Conference on Artifical Intellegence and Statistics, [Online] Mar. 21, 2007-Mar. 24, 2007, XP007903848 Online ISBN: 0-9727358-2-8, Retrieved from the Internet: URL:http://www.stat.umn.edu/{aistat/proceedings/data/papers/077.pdf> [retrieved on Jan. 21, 2007.
Shental, Noam; Hertz, Tomer; Weinshall, Daphna; Pavel, Misha, “Adjustment Learning and Relevant Component Analysis”, Proceedings of ECCV, [Online] Jun. 2002, XP007903913, Copenhagen, DK, Retrieved from the Internet: URL:http://citeseer.ist.psu.edu/cache/papers/cs/26471/http:zSzzSzleibniz.cs.huji.ac.ilzSztrzSzacczSz2002zSzHUJI-CSE-LTR-2002-16—paper—CR01.pdf/shental02adjustment.pdf> [retrieved on Jan. 24, 2008].
Scott Cost, et al., “A Weighted Nearest Neighbor Algorithm for Learning with Symobolic Features”, Machine Learning, Kluwer Academic Publishers—Plenum Publishers, NE, vol. 10, No. 1, Jan. 1, 1993, pp. 57-78, XP019213229 ISSN: 1573-0565.
Lowe D G, “Similarity Metric Learning for A Variable-Kernel Classifier”, Internet Citation, [Online] Nov. 25, 1993, XP007903889, retrieved from the Internet: http://citeseer.ist.psu.edu/cache/papers/cs/286/http:zSzzSzwww.cs.ubc.cazSzspiderzSzlowezSzpaperszSzneural95.pdf/lowe95similarity.pdf> [retrieved on Jan. 21, 2008].

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