Maximizing expected generalization for learning complex...

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C707S793000, C707S793000

Reexamination Certificate

active

10116383

ABSTRACT:
A method of learning a user query concept is provided which includes a sample selection stage and a feature reduction stage; during the sample selection stage, sample objects are selected from a query concept sample space bounded by a k-CNF and a k-DNF; the selected sample objects include feature sets that are no more than a prescribed amount different from a corresponding feature set defined by the k-CNF; during the feature reduction stage, individual features are removed from the k-CNF that are identified as differing from corresponding individual features of sample objects indicated by the user to be close to the user's query concept; also during the feature reduction stage, individual features are removed from the k-DNF that are identified as not differing from corresponding individual features of sample objects indicated by the user to be not close to the user's query concept.

REFERENCES:
patent: 5259067 (1993-11-01), Kautz et al.
patent: 5265207 (1993-11-01), Zak et al.
patent: 5414853 (1995-05-01), Fertig et al.
patent: 5636328 (1997-06-01), Kautz et al.
patent: 5666528 (1997-09-01), Thai
patent: 6006225 (1999-12-01), Bowman et al.
patent: 6263335 (2001-07-01), Paik et al.
patent: 6377945 (2002-04-01), Risvik
patent: 6408293 (2002-06-01), Aggarwal et al.
patent: 6418432 (2002-07-01), Cohen et al.
patent: 6535873 (2003-03-01), Fagan et al.
patent: 6662235 (2003-12-01), Callis et al.
patent: 6675159 (2004-01-01), Lin et al.
patent: 6714201 (2004-03-01), Grinstein et al.
patent: 6728952 (2004-04-01), Carey et al.
patent: 2004/0267458 (2004-12-01), Judson et al.
patent: 361248130 (1986-11-01), None
patent: 401244528 (1989-09-01), None
patent: 03040170 (1991-02-01), None
Daniel S. Hirschberg, Michael J. Pazzani, Kamal M. Ali, “Average Case Analysis of k-CNF and k-DNF learning algorithms”, Jul. 1, 1994, Department of Information and Computer Science University of California, pp. 1-18.
Leonard Pitt, Leslie G. Valiant, “Computational Limitations on Learning from Examples”, ACM, 1998, pp. 965-984.
B.K. Natarajan, “On Learning Boolean Functions”, ACM, 1987, pp. 296-304.
Nina Mishra, Leonard Pitt, “Generating all Maximal Independent Sets of Bounded-degree Hypergraphs”, ACM, 1997, pp. 211-217.
Aho, Alfred V. et al. (1994) “Foundations of Computer Science,” Computer Science Press, and imprint of W.H. Freeman and Company, New York, pp. 634-636, 667-670.
Billard, David (Aug. 26-28, 1998) “Multipurpose Internet Shopping Basket,” Database and Expert Systems Applications. Proceedings of the Ninth International Workshop, pp. 685-690.
Djeraba, Chabane et al. (Apr. 22-24, 1998) “Concept-Based Query in Visual Information Systems,” Research and Technology Advances in Digital Libraries, pp. 299-308.
Mitchell, Tom M. ((1997) “Machine Learning,” The McGraw-Hill Companies, Inc., pp. 20-51, 213-215.
E. Chang et al., “MEGA—The Maximizing Expected Generalization Algorithm for Learning Complex Query Concepts”, Technical Report, Nov. 2000, pp. 1-39.
M. Kearns et al., “Learning Boolean formulae”, Journal of ACM; 41(6):1298-1328, 1994.
M. Kearns and U. Vazirani, “An Introduction to Computational Learning Theory”, MIT Press, 3 pgs. 1994.
P. Langley and W. Iba, “Average-case analysis of a nearest neighbor algorithm”, Proceedings of the 13th International Joint Conference on Artificial Intelligence, (82):889-894, 1993.
P. Langley and S. Sage, “Scaling to domains with many irrelevant features”, Computational Learning Theory and Natural Learning Systems, 29 pgs., 1997.
C. Li, et al., “Clustering for approximate similarity queries in high-dimensional spaces”, IEEE Transaction on Knowledge and Data Engineering, 41 pgs., 2001.
T. Michell, “Machine Learning”, McGraw Hill, 1997.
M. Ortega, “Supporting ranked Boolean similarity queries in MARS”, IEEE Transaction on Knowledge and Data Engineering, 10(6):905-925, Dec. 1999.
K. Porkaew et al., “Query refinement for multimedia similarity retrieval in MARS”, Proceedings of ACM Multimedia, 12 pgs., Nov. 1999.
K. Porkaew et al., “Query reformulation for content based multimedia retrieval in MARS”, ICMCS, 18 pgs., 1999.
Y. Rui et al., “Image retrieval: Current techniques, promising directions, and open issues”, Journal of Visual Communication and Image Representation, 17 pgs., Mar. 1999.
Y. Rui et al., “Relevance feedback: A power tool in interactive content-based image retrieval”, IEEE Tran on Circuits and Systems for Video Technology, 8(5), 13 pgs. Sep. 1998.
J. R. Smith et al., “Tools and Techniques for Color Image Retrieval”, IS&T/SPIE Proceedings vol. 2670, Stor. & Retr. for Image and Video Databases IV, 1996, 12 pgs.
L. Valiant, “A theory of learnable”, Proceedings of the Sixteenth Annual ACM Symposium on Theory of Computing, pp. 436-445, 1984.
L. Wu et al., “Falcon: Feedback adaptive loop for content-based retrieval”, The 26th VLDB Conference, 10 pgs., Sep. 2000.
L. A. Zadeh, “Fuzzy sets”, Information and Control, pp. 338-353, 1965.
U.S. Appl. No. 60/281,053, filed Apr. 2, 2001, Chang et al.
U.S. Appl. No. 60/324,766, filed Sep. 24, 2001, Chang.
U.S. Appl. No. 60/292,820, filed May 22, 2001, Chang et al.
E. Chang and T. Cheng, “Perception-based image retrieval”, ACM Sigmod (Demo), May 2001.
E. Chang, B. Li, “Towards perception-based image retrieval”, IEEE, Content-Based Access of Image and Video Libraries, 5 pgs, Jun. 2000.
I. J. Cox, et al., “The Bayesian image retrieval system, Pichunter: Theory, implementation and psychological experiments”, IEEE Transaction on Image Processing, 19 pgs. 2000.
R. Fagin, “Fuzzy queries in multimedia database systems”, ACM Sigacr-Sigmod-Sigart Symposium on Principles of Database Systems, 22 pgs., 1998.
R. Fagin and E. L. Wimmers, “A formula for incorporating weights into scoring rules”, International Conference on Database Theory, 28 pgs, 1997.
J. Flusser et al., “On the Calculation of Image Moments”.
Y. Freund, et al., “Selective sampling using the query by committee algorithm”, Machine Learning, 28:133-168, 1997.
Y. Ishikawa et al., “Mindreader: Querying databases through multiple examples”, VLDB, 22 pgs., 1998.

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

Maximizing expected generalization for learning complex... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Maximizing expected generalization for learning complex..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Maximizing expected generalization for learning complex... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3764052

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