Patents by Inventor Pannagadatta SHIVASWAMY

Pannagadatta SHIVASWAMY 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).

  • High accuracy learning by boosting weak learners
    Patent number: 9607246
    Abstract: A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.
    Type: Grant
    Filed: July 30, 2013
    Date of Patent: March 28, 2017
    Assignee: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
    Inventors: Tony Jebara, Pannagadatta Shivaswamy
  • HIGH ACCURACY LEARNING BY BOOSTING WEAK LEARNERS
    Publication number: 20140029840
    Abstract: A system, apparatus, method, and computer-readable medium for optimizing classifiers are disclosed. The optimization process can include receiving one or more training examples. The optimization process can further include assigning a loss parameter to each training example. The optimization process can further include optimizing each loss parameter of each training sample based on a sample variance of each training example using a non-linear function. The optimization process can further include estimating a classifier from the one or more weighted training samples. The optimization process can further include assigning a loss parameter to the classifier based on a number of training examples that the classifier correctly classified and a number of training examples that the classifier incorrectly classified. The optimization process can further include adding the weighted classifier to an overall classifier.
    Type: Application
    Filed: July 30, 2013
    Publication date: January 30, 2014
    Applicant: The Trustees of Columbia University in the City of New York
    Inventors: Tony JEBARA, Pannagadatta SHIVASWAMY