Patents by Inventor Kristin Kendall Snow

Kristin Kendall Snow 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).

  • Machine learning systems and methods
    Patent number: 7389277
    Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.
    Type: Grant
    Filed: July 8, 2005
    Date of Patent: June 17, 2008
    Assignee: Medical Scientists, Inc.
    Inventors: Hung-Han Chen, Lawrence Hunter, Harry Towsley Poteat, Kristin Kendall Snow
  • Machine learning method
    Patent number: 6917926
    Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.
    Type: Grant
    Filed: June 15, 2001
    Date of Patent: July 12, 2005
    Assignee: Medical Scientists, Inc.
    Inventors: Hung-Han Chen, Lawrence Hunter, Harry Towsley Poteat, Kristin Kendall Snow
  • Machine learning method
    Publication number: 20030018595
    Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.
    Type: Application
    Filed: June 15, 2001
    Publication date: January 23, 2003
    Inventors: Hung-Han Chen, Lawrence Hunter, Harry Towsley Poteat, Kristin Kendall Snow