Accession Number:

ADA582481

Title:

The Exponentially Embedded Family of Distributions for Effective Data Representation, Information Extraction, and Decision Making

Descriptive Note:

Final rept. 30 Sep 2011-20 Dec 2012

Corporate Author:

RHODE ISLAND UNIV KINGSTON

Personal Author(s):

Report Date:

2013-03-01

Pagination or Media Count:

41.0

Abstract:

We have focused on the mathematical formalism and stochastic machine learning algorithms for extraction of relevant information based on the exponentially embedded family EEF, learning and classification over large-scale stream data, and information fusion and integration. In particular, we have proposed a probability density function PDF estimation approach based on the EEF, and a measure for assessment of information from sensors. We have also taken advantage of the model structure information for model estimation. Furthermore, we have proved a general Pythagorean theorem for the EEF and studied a multi path scenario for sensor selection. Finally, we also analyzed and developed a series of machine learning techniques for effective data learning, classification, and decision making, including adaptive incremental learning from stream data, information fusion with multiple learning modelshypotheses, machine learning with non-stationary imbalanced stream data, kernel density estimation based on self-organizing mapSOM, among others. These results have been published in peer-reviewed conferences and journals, including IEEE Transactions on Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing Elsevier, a book chapter with Wiley-IEEE, among others.

Subject Categories:

  • Administration and Management
  • Information Science
  • Manufacturing and Industrial Engineering and Control of Production Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE