Efficiency of training for ranking systems based on pairwise...

Data processing: artificial intelligence – Neural network

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S048000, C707S793000

Reexamination Certificate

active

07617164

ABSTRACT:
The subject disclosure pertains to systems and methods for facilitating training of machine learning systems utilizing pairwise training. The number of computations required during pairwise training is reduced by grouping the computations. First, a score is generated for each retrieved data item. During processing of the data item pairs, the scores of the data items in the pair are retrieved and used to generate a gradient for each data item. Once all of the pairs have been processed, the gradients for each data item are aggregated and the aggregated gradients are used to update the machine learning system.

REFERENCES:
patent: 5493692 (1996-02-01), Theimer et al.
patent: 5544321 (1996-08-01), Theimer et al.
patent: 5555376 (1996-09-01), Theimer et al.
patent: 5603054 (1997-02-01), Theimer et al.
patent: 5611050 (1997-03-01), Theimer et al.
patent: 5625751 (1997-04-01), Brandwajn et al.
patent: 5649068 (1997-07-01), Boser et al.
patent: 5812865 (1998-09-01), Theimer et al.
patent: 6260013 (2001-07-01), Sejnoha
patent: 6466232 (2002-10-01), Newell et al.
patent: 6513046 (2003-01-01), Abbott, III et al.
patent: 6526440 (2003-02-01), Bharat
patent: 6549915 (2003-04-01), Abbott, III et al.
patent: 6636860 (2003-10-01), Vishnubhotla
patent: 6691106 (2004-02-01), Sathyanarayan
patent: 6747675 (2004-06-01), Abbott et al.
patent: 6785676 (2004-08-01), Oblinger
patent: 6791580 (2004-09-01), Abbott et al.
patent: 6801223 (2004-10-01), Abbott et al.
patent: 6812937 (2004-11-01), Abbott et al.
patent: 6842877 (2005-01-01), Robarts et al.
patent: 6873990 (2005-03-01), Oblinger
patent: 6968333 (2005-11-01), Abbott et al.
patent: 7249058 (2007-07-01), Kim et al.
patent: 7281002 (2007-10-01), Farrell
patent: 7305381 (2007-12-01), Poppink et al.
patent: 2001/0040590 (2001-11-01), Abbott et al.
patent: 2001/0040591 (2001-11-01), Abbott et al.
patent: 2001/0043231 (2001-11-01), Abbott et al.
patent: 2001/0043232 (2001-11-01), Abbott et al.
patent: 2002/0032689 (2002-03-01), Abbott, III et al.
patent: 2002/0044152 (2002-04-01), Abbott, III et al.
patent: 2002/0052930 (2002-05-01), Abbott et al.
patent: 2002/0052963 (2002-05-01), Abbott et al.
patent: 2002/0054130 (2002-05-01), Abbott, III et al.
patent: 2002/0054174 (2002-05-01), Abbott et al.
patent: 2002/0078204 (2002-06-01), Newell et al.
patent: 2002/0080155 (2002-06-01), Abbott et al.
patent: 2002/0080156 (2002-06-01), Abbott et al.
patent: 2002/0083025 (2002-06-01), Robarts et al.
patent: 2002/0083158 (2002-06-01), Abbott et al.
patent: 2002/0087525 (2002-07-01), Abbott et al.
patent: 2002/0099817 (2002-07-01), Abbott et al.
patent: 2002/0152190 (2002-10-01), Biebesheimer et al.
patent: 2002/0188589 (2002-12-01), Salmenkaita et al.
patent: 2003/0046401 (2003-03-01), Abbott et al.
patent: 2003/0154476 (2003-08-01), Abbott, III et al.
patent: 2003/0187844 (2003-10-01), Li et al.
patent: 2003/0236662 (2003-12-01), Goodman
patent: 2005/0034078 (2005-02-01), Abbott et al.
patent: 2005/0049990 (2005-03-01), Milenova et al.
patent: 2005/0125390 (2005-06-01), Hurst-Hiller et al.
patent: 2005/0144158 (2005-06-01), Capper et al.
patent: 2007/0043706 (2007-02-01), Burke et al.
patent: 2007/0124297 (2007-05-01), Toebes
patent: 9800787 (1998-01-01), None
Joachims, T. “Optimizing Search Engines using Clickthrough Data,” ACM SIGKDD, 02, pp. 133-142.
Joachims “Optimizing Search Engines using Clickthrough Data”, ACM SIGKDD, 2002, pp. 133-142.
Erdogmus, et al. “Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation”, Natural Computing, vol. 1, Issue 1, 2002, pp. 85-108.
Richardson, et al. “The Intelligent Surfer: Probabilistic Combination of Link andContent Information in PageRank”, In Advances in Neural Information Processing Systems 14 (2002).
Joachims “Evaluating Retrieval Performance using Clickthrough Data”, Text Mining, 2003, pp. 79-96.
Storn, et al., “Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, 1996.
Xia, et al., “A One-Layer Recurrent Neural Network for Support Vector Machine Learning”, 2004.
Cohen, et al., “Volume Seedlings”, 1992.
Storn, “On the Usage of Differential Evolution for Function Optimization”, 2002.
Jarvelin, et al., Cumulated Gain-Based Evaluation of IR Techniques, 2002.
International Search Report and Written Opinion dated Mar. 6, 2008 for PCT Application Serial No. PCT/US06/26266, 11 Pages.
Storn, et al. “Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, 1996.
Storn. “On the Usage of Differential Evolution for Function Optimization”, 2002.
Xia, et al. “A One-Layer Recurrent Neural Network for Support Vector Machine Learning”, 2004.
U.S. Appl. No. 11/305;395, Burges, et al.
U.S. Appl. No. 11/066,514, Burges, et al.
G. S. Kimeldorf, et al., “Some results on Tchebycheffian Spline Functions” J. Mathematical Analysis and Applications, 1971, vol. 33, pp. 82-95.
C. Burges, et al, “Learning to Rank Using Gradient Descent”, Proceedings of the 22nd international conference on Machine learning, ACM International Conference Proceeding Series; 2005, pp. 89-96, vol. 119, Bonn, Germany.
C. Burges, “Ranking as Learning Structured Outputs”, in Proceedings of the NIPS 2005 Workshop on Learning to Rank, Dec. 2005, 4 pages.
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 1, 100 pages (front cover-80).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 2, 100 pages (81-180).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 3, 100 pages (181-280).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 4, 100 pages (281-380).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 5, 100 pages (381-480).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 6, 100 pages (481-580).
B. Scholkopf, et al., “Learning with Kernels”, MIT Press, 2002, pt. 7, 49 pages (581-back cover).
Andy Harter, et al., A Distributed Location System for the Active Office, IEEE Network, 1994, pp. 62-70.
Guanling Chen, et al., A Survey of Context-Aware Mobile Computing Research, Dartmouth Computer Science Technical Report, 2000, 16 pages.
William Noah Schilt, A System Architecture for Context-Aware Mobile Computing, Columbia University, 1995, 153 pages.
Mike Spreitzer, et al., Providing Location Information in a Ubiquitous Computing Environment, SIGOPS '93, 1993, pp. 270-283.
Marvin Theimer, et al., Operating System Issues for PDAs, In Fourth Workshop on Workstation Operating Systems, 1993, 7 pages.
Roy Want, Active Badges and Personal Interactive Computing Objects, IEEE Transactions on Consumer Electronics, 1992, 11 pages, vol. 38- No. 1.
Bill N. Schilit, et al., The ParcTab Mobile Computing System, IEEE WWOS-IV, 1993, 4 pages.
Bill Schilit, et al., Context-Aware Computing Applications, In Proceedings of the Workshop on Mobile Computing Systems and Applications, Dec. 1994. pp. 85-90.
Bill N. Schilit, et al., Customizing Mobile Applications, Proceedings USENIX Symposium on Mobile and Location Independent Computing, Aug. 1993, 9 pages.
Mike Spreitzer, et al., Architectural Considerations for Scalable, Secure, Mobile Computing with Location Information, In The 14th International Conference on Distributed Computing Systems, Jun. 1994, pp. 29-38.
Mike Spreitzer et al., Scalable, Secure, Mobile Computing with Location Information, Communications of the ACM, Jul. 1993, 1 page, vol. 36—No. 7.
Roy Want, et al., The Active Badge Location System, ACM Transactions on Information Systems, Jan. 1992, pp. 91-102, vol. 10—No. 1.
Mark Weiser, Some Computer Science Issues in Ubiquitous Computing, Communications of the ACM, Jul. 1993, pp. 75-84, vol. 36—No. 7.
M. Billinghurst, et al., An Evaluation of Wearable Info

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Efficiency of training for ranking systems based on pairwise... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Efficiency of training for ranking systems based on pairwise..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Efficiency of training for ranking systems based on pairwise... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4131939

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.