The Exponentially Embedded Family of Distributions for Effective Data Representation, Information Extraction, and Decision Making
Final rept. 30 Sep 2011-20 Dec 2012
RHODE ISLAND UNIV KINGSTON
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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.
- Administration and Management
- Information Science
- Manufacturing and Industrial Engineering and Control of Production Systems