Method and apparatus for incorporating decision making into...

Data processing: artificial intelligence – Neural network – Learning task

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S015000, C706S016000

Reexamination Certificate

active

06920439

ABSTRACT:
A method and a system are presented, which assist classifiers in gathering information in a cost-effective manner by determining which piece of information, if any, to gather next. The system includes an explicit system, an implicit system, a classifier, and a profit module. A feature set is inputted into the explicit system, which uses the feature set to determine tests to perform to gather information useful for classifying the system state. The relative profit of each test performed is determined by the profit module and the profit determined is used to determine which test or tests to select for a particular feature set. The results of the explicit system, which is generally an exhaustive or semi-exhaustive search algorithm, are used to train the implicit system. The implicit system is then able to process decisions much faster than the explicit system when circumstances require time-critical operation.

REFERENCES:
patent: 4731725 (1988-03-01), Suto et al.
patent: 5303328 (1994-04-01), Masui et al.
patent: 6556977 (2003-04-01), Lapointe et al.
patent: WO97 29447 (1997-08-01), None
patent: WO98 22884 (1998-05-01), None
patent: WO99 09506 (1999-02-01), None
Nir Friedman and Moises Goldszmidt, “Learning Bayesian networks with local structure,” In Eric Horvitz and Finn Jensen, editors, Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI-96), pp. 252-262, San Francisco, Aug. 1-4, 1996, Morgan Kaufmann Publishers.
John R. Anderson and Michael Matcssa, “Explorations of an incremental, Bayesian algorithm for categorization,” Machine Learning, 9:275-308, 1992.
J. Moody and C.J. Darken, “Fast learning in networks of locally-tuned processing units,” Neural Computation, 1(2):281-294, 1989.
S. Kirkpatrick, C. D. Gelatt, and M.P. Vecchi, “Optimization by simulated annealing,” Science, May 13, 1983, 220(4598): 671-680, 1983.
David Heckerman, “A tutorial on learning Bayesian networks,” Technical Report MSR-TR-95-06, Microsoft Research, Mar. 1995.
K.L. Poh and E. Horvitz, “Topological proximity and relevance in graphical decision models,” Technical Report MSR-TR-95-15, Research, Advanced Technology Division, 1995.
Marlon Nunez, “The use of background knowledge in decision tree induction,” Machine Learning, 6:231-250, 1991.
Ming Tan, “Cost-sensitive learning of classification knowledge and its applications in robotics,” Machine Learning, 13:7-33, 1993.
J.R. Anderson, “The adaptive nature of human categorization,” Psychological Review, 98: 409-429, 1991.
John K. Kruschke, “ALCOVE: An exemplar-based connectionist model of category learning,” Psychological Review, 99(1):22-44, Jan. 1992.
Nir Friedman, “Learning belief networks in the presence of missing values and hidden variables,” In Proc. 14th International Conference on Machine Learning, pp. 125-133, Morgan Kaufmann, 1997.
P. D. Turney, “Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm,” Journal of Artifical Intelligence Research, 2:369-409, 1995.
Avirm Blum and Pat Langley, “Selection of relevant features and examples in machine learing,” Artificial Intelligence, 99: 99-99, 1998.
David Madigan, Krzysztof Mosurski, and Russell G. Almond, “Graphical explanation in belief networks,” Journal of Computational and Graphical Statistics, 6(2):160-181, Jun. 1997.
Eric Horvitz and Adam Seiver, “Time-critical action: Respresentations and application,” In Dan Geiger and Prakash Pundalik Shenoy, editors, Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence (UAI-97), pp. 250-257, San Francisco, Aug. 1-3, 1997, Morgan Kaufmann Publishers.
Steven W. Norton, “Generating better decision trees,” In N.S. Sridharan, editor, Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 800-805, Detroit, MI, USA, Aug. 1989, Morgan Kaufmann.

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

Method and apparatus for incorporating decision making into... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Method and apparatus for incorporating decision making into..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus for incorporating decision making into... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3381123

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