Patents by Inventor Marsha Prescott Duro

Marsha Prescott Duro has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Adapting Bayesian network parameters on-line in a dynamic environment
    Patent number: 7685278
    Abstract: A method for adapting a Bayesian network includes determining a set of parameters for the Bayesian network, for example, initial parameters, and then updating the parameters in response to a set of observation data using an adaptive learning rate. The adaptive learning rate responds to any changes in the underlying modeled environment using minimal observation data.
    Type: Grant
    Filed: December 18, 2001
    Date of Patent: March 23, 2010
    Assignee: Hewlett-Packard Development Company, L.P.
    Inventors: Ira Cohen, Alexandre Bronstein, Marsha Prescott Duro
  • Learning bayesian network classifiers using labeled and unlabeled data
    Publication number: 20040220892
    Abstract: A method that yields more accurate Bayesian network classifiers when learning from unlabeled data in combination with labeled data includes learning a set of parameters for a structure of a classifier using a set of labeled data and learning a set of parameters for the structure using the labeled data and a set of unlabeled data and then modifying the structure if the parameters based on the labeled and unlabeled data leads to less accuracy in the classifier in comparison to the parameters based on the labeled data only. The present technique enable an increase in the accuracy of a statistically learned Bayesian network classifier when unlabeled data are available and reduces the likelihood of degrading the accuracy of the Bayesian network classifier when using unlabeled data.
    Type: Application
    Filed: April 29, 2003
    Publication date: November 4, 2004
    Inventors: Ira Cohen, Fabio G. Cozman, Alexandre Bronstein, Marsha Prescott Duro
  • Adapting Bayesian network parameters on-line in a dynamic environment
    Publication number: 20030115325
    Abstract: A method for adapting a Bayesian network includes determining a set of parameters for the Bayesian network, for example, initial parameters, and then updating the parameters in response to a set of observation data using an adaptive learning rate. The adaptive learning rate responds to any changes in the underlying modeled environment using minimal observation data.
    Type: Application
    Filed: December 18, 2001
    Publication date: June 19, 2003
    Inventors: Ira Cohen, Alexandre Bronstein, Marsha Prescott Duro