Split machine learning systems

Details Split machine learning systems Split machine learning systems

2005-03-31

2008-09-02

Vincent, David (Department: 2129)

Data processing: artificial intelligence

Machine learning

C706S016000, C706S025000, C359S011000, C359S107000, C382S209000

Reexamination Certificate

active

07421414

ABSTRACT:
Split machine learning systems can be used to generate an output for an input. When the input is received, a determination is made as to whether the input is within a first, second, or third range of values. If the input is within the first range, the output is generated using a first machine learning system. If the input is within the second range, the output is generated using a second machine learning system. If the input is within the third range, the output is generated using the first and second machine learning systems.

REFERENCES:
patent: 5479573 (1995-12-01), Keeler et al.
patent: 5793480 (1998-08-01), Lacey et al.
patent: 5889593 (1999-03-01), Bareket
patent: 6023327 (2000-02-01), Shabde et al.
patent: 6192103 (2001-02-01), Wormington et al.
patent: 6609086 (2003-08-01), Bao et al.
patent: 6633831 (2003-10-01), Nikoonahad et al.
patent: 6650422 (2003-11-01), Singh et al.
patent: 6657736 (2003-12-01), Finarov et al.
patent: 6665446 (2003-12-01), Kato et al.
patent: 6704661 (2004-03-01), Opsal et al.
patent: 6771356 (2004-08-01), Lyons et al.
patent: 6782337 (2004-08-01), Wack et al.
patent: 6785638 (2004-08-01), Niu et al.
patent: 6943900 (2005-09-01), Niu et al.
patent: 6999254 (2006-02-01), Phan et al.
patent: 7043397 (2006-05-01), Johnson
patent: 7072049 (2006-07-01), Niu et al.
patent: 7092110 (2006-08-01), Balasubramanian et al.
patent: 7126700 (2006-10-01), Bao et al.
patent: 7158896 (2007-01-01), Singh et al.
patent: 7186650 (2007-03-01), Dakshina-Murthy
patent: 7216045 (2007-05-01), Doddi et al.
patent: 7224456 (2007-05-01), Phan et al.
patent: 7224471 (2007-05-01), Bischoff et al.
patent: 7280229 (2007-10-01), Li et al.
patent: 2004/0017574 (2004-01-01), Vuong et al.
patent: 2004/0267397 (2004-12-01), Doddi et al.
patent: 2005/0057748 (2005-03-01), Vuong et al.
patent: 2005/0192914 (2005-09-01), Drege et al.
patent: 2005/0209816 (2005-09-01), Vuong et al.
patent: 2007/0185684 (2007-08-01), Vuong et al.
patent: 0448890 (1991-10-01), None
Sarkar,M. “Modular Pattern Classifiers: A Brief Survey” IEEE 2000.
Brooks, R.A “How to Build Complete Creatures Rather than Isolated Cognitive Simulators” MIT AI Lab. 1991.
Horswill,I.D. “Functional Programming of Behavior-Based Systems” 2000.
Horswill,I.D. “Conflict Resolution” 2000.
Dietterich,T.G. “Machine Learning Research: Four Current Directions” 1997.
van Halteren,H. et al. “Improving Accuracy in Wordclass Tagging through Combination of Machine Learning Systems” Computational Linguistics. 2001.
del Jesus,M.J. et al. “Induction of Fuzzy-Rule-Based Classifiers with Evolutionary Boosting Algorithms” IEEE Trans. on Fuzzy Systems, vol. 12, No. 3. Jun. 2004.
Goodridge,S.G. et al. “Fuzzy Behavior Fusion for Reactive Control of an Autonomous Mobile Robot: MARGE” IEEE. 1994.
Rowe,N.C. “Plan Fields and Real-World Uncertainty” AAAI Spring Symposium on Planning in Uncertain, Unpredictable, or Changing Environments. Mar. 1990. Accessed using Archive.org on Jun. 21, 2007; Archive version: Nov. 21. 2002.
Haykin, S. (1999).Neural Networks. 2nd edition, M. Horton ed., Prentice Hall: Upper Saddle River, New Jersey, 9 pages (Table of Contents).
Arthur, G. G. et al. (1997). “Enhancing the Development Rate Model for Optimum Simulation Capability in the Subhalf-Micron Regime,”Proceedings of SPIE3049:189-200.
Gahegan, M. et al. (1999). “Dataspaces as an Organizational Concept for the Neural Classification of Geographic Datasets,” GeoComputation, located at <http://www.geovista.psu.edu/sites/geocomp99/Gc99/011/gc—011.htm>visited on Aug. 14, 2007, (8pages).
International Search Report and Written Opinion mailed May 16, 2005, for PCT Application No. PCT/US04/20682 filed Jun. 25, 2004, 7 pages.
Keeman, V. (2005). “Support Vector Machine - An Introduction”In Support Vector Machines: Theory and Applications. Wang, L. ed., Springer-Verlag Berlin Heidelberg: The Netherlands, pp. 1-47.
Platt, J. C. (1999). “Fast Training of Support Vector Machines Using Sequential Minimal Optimization” Chapter 12In Advances in Kernel Methods: Support Vector Learning. Schölkopf et al. eds., MIT Press: Cambridge, MA, pp. 185-208.
Press, W. H. et al. (1992).Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 2nd edition, 8 pages (Table of Contents).
United States Patent Application No. 11/729,498, filed Mar. 28, 2007 for Bischoff et al.
United States Patent Application No. 11/786,869, filed Apr. 12, 2007 for Jin et al.
United States Patent Application No. 11/786,870, filed Apr. 12, 2007 for Jin et al.
United States Patent Application No. 11/787,025, filed Apr. 12, 2007 for Jin et al.
Vapnik, V. N. (1998).Statistical Learning Theory. John Wiley & Sons, Inc., 15 pages (Table of Contents).

Affiliated with

Also associated with

Rating

Split machine learning systems does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Split machine learning systems, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Split machine learning systems will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3991929