Staged mixture modeling

Details Staged mixture modeling Staged mixture modeling

2006-11-07

2006-11-07

Thangavelu, Kandasamy (Department: 2123)

Data processing: structural design, modeling, simulation, and em

Modeling by mathematical expression

C707S793000, C707S793000, C707S793000, C707S793000, C706S052000, C702S019000

Reexamination Certificate

active

07133811

ABSTRACT:
A system and method for generating staged mixture model(s) is provided. The staged mixture model includes a plurality of mixture components each having an associated mixture weight, and, an added mixture component having an initial structure, parameters and associated mixture weight. The added mixture component is modified based, at least in part, upon a case that is undesirably addressed by the plurality of mixture components using a structural expectation maximization (SEM) algorithm to modify at the structure, parameters and/or associated mixture weight of the added mixture component.The staged mixture model employs a data-driven staged mixture modeling technique, for example, for building density, regression, and classification model(s). The basic approach is to add mixture component(s) (e.g., sequentially) to the staged mixture model using an SEM algorithm.

REFERENCES:
patent: 6263334 (2001-07-01), Fayyad et al.
patent: 6263337 (2001-07-01), Fayyad et al.
patent: 6336108 (2002-01-01), Thiesson et al.
patent: 6345265 (2002-02-01), Thiesson et al.
patent: 6374251 (2002-04-01), Fayyad et al.
patent: 6408290 (2002-06-01), Thiesson et al.
patent: 6449612 (2002-09-01), Bradley et al.
patent: 6496816 (2002-12-01), Thiesson et al.
patent: 6529891 (2003-03-01), Thiesson et al.
patent: 6581058 (2003-06-01), Fayyad et al.
patent: 6591235 (2003-07-01), Chen et al.
patent: 6633882 (2003-10-01), Fayyad et al.
patent: 6807537 (2004-10-01), Thiesson et al.
patent: 6862574 (2005-03-01), Srikant et al.
patent: 6964023 (2005-11-01), Maes et al.
patent: 7003158 (2006-02-01), Bennett et al.
patent: 2002/0107853 (2002-08-01), Hofmann et al.
patent: 2002/0129038 (2002-09-01), Cunningham
patent: 2002/0135618 (2002-09-01), Maes et al.
patent: 2002/0138492 (2002-09-01), Kil
patent: 2003/0014399 (2003-01-01), Hansen et al.
patent: 2003/0065635 (2003-04-01), Sahami et al.
patent: 2003/0100998 (2003-05-01), Brunner et al.
patent: 2004/0009489 (2004-01-01), Golub et al.
patent: 2004/0010497 (2004-01-01), Bradley et al.
patent: 2004/0117367 (2004-06-01), Smith et al.
patent: 2005/0089923 (2005-04-01), Levinson et al.
Beaufays et al., “Discriminative mixture weight estimation for large Gaussian mixture models”, IEEE, 1999.
Yingjian et al., “A global learning method for RBFN”, IEEE, Aug. 2002.
Beeferman et al., “A model of lexical attraction and repulsion.”, ACM, Jul. 1997.
Perira et al., “An efficient extension to mixture techniques for prediction and decision trees”, ACM 1997.
Ordonez et al., “SQLEM: fast clustering in SQL using EM algorithm”, ACM 2000.
Jonathan Q. Li, et al.; “Mixture Density Estimation”; In S.A. Solla, T.K. Leen, and K.R. Muller, Editors, Advances in Neural Information Processing Systems 12, pp. 279-285. MIT Press, 2000.
Bo Thiesson, et al.; “Learning Mixtures of DAG Models”; Dec. 1997 (Revised May 1998); Technical Report MSR-TR-97-30; pp. 1-28.
Nir Friedman; “The Bayesian Structural EM Algorithm”, 1998.
David Maxwell Chickering, David Heckerman and Christopher Meek, A Bayesian approach to learning Bayesian networks with local structure, Technical Report: MSR-TR-97-07, Aug. 1997, pp. 1-19.
David Maxwell Chickering, Christopher Meek and Robert Rounthwaite, Efficient Determination of Dynamic Split Points in a Decision Tree, IEEE Computer Society, 2001, 8 Pages.
Yoav Freund and Robert E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting*, Journal of Computer and System Sciences, Dec. 19, 1996, 35 Pages.
Jerome H. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, Technical Report, Department of Statistics, Stanford University, 1999, 34 Pages.
Jerome Friedman, Trevor Hastie and Robert Tibshirani, Additive Logistic Regression: a Statistical View of Boosting, Technical Report, Department of Statistics, Stanford University, Aug. 17, 1998, 44 Pages.
Nir Friedman, Learning Belief Networks in the Presence of Missing Values and Hidden Variables, 9 Pages.
D. Greiger, D. Heckerman, H. King and C. Meek, Stratified Exponential Families: Graphical Models and Model Selection, Technical Report: MSR-TR-98-31, Jul. 1998, 30 Pages.
Geoffrey E. Hinton, Peter Dayan and Michael Revow, Modelling the Manifolds of Images of Handwritten Digits, IEEE Transactions on Neutral Networks,1997, 29 Pages.
X. Huang, A. Acero, F. Alleva, M. Hwang, L. Jiang and M. Mahajan, Microsoft Windows Highly Intelligent Speech Recognizer: Whisper, In IEEE International Conference on Acoustics, Speech, and Signal Processing, 1995, ICASSP-95, vol. 1, pp. 93-96.
C. Meek, D.M. Chickering and D. Heckerman, Autoregressive Tree Models for Time-Series Analysis, Proceedings of the Second International SIAM Conference on Data Mining, Arlington, VA. SIAM, 16 Pages.
M. Meila and M. Jordan, Learning with Mixtures of Trees, Journal of Machine Learning Research 1, 2000: pp. 1-48.
B. Thiesson, C. Meek, D.M. Chickering and D. Heckerman, Computationally Efficient Methods for Selecting Among Mixtures of Graphical Models, Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting, Clarendon Press, Oxford, 18 Pages.
Christopher Meek, Bo Thiesson and David Heckerman, Staged Mixture Modeling and Boosting, Technical Report: MSR-TR-2002-45, Feb. 2002, 12 Pages.

Affiliated with

Also associated with

Rating

Staged mixture modeling does not yet have a rating. At this time, there are no reviews or comments for this patent.

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

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-3644372