Adaptive Mixture Approach to Pattern Recognition
Final rept. Oct 1989-Sep 1990
NAVAL OCEAN SYSTEMS CENTER SAN DIEGO CA
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A large number of pattern recognition require the ability to recognize patterns within data when the character of the patterns may change with time. Examples of such tasks are remote sensing, autonomous control, and automatic target recognition in a changing environment. Titterington et al give a list of tasks to which mixture models have been applied. Many of these tasks, and their variants, fall into the above categories. These tasks have a common requirements the need to recognize new entities as they enter the environment. A pattern recognition system must be able to recognize and develop a representation of a new pattern in the environment as well as to change its representation of the statistics of the pattern dynamically. The adaptive mixtures approach presented here uses density estimation to develop decision functions for supervised and unsupervised learning. Much work in performing density estimation in supervised situations has been done. For the most part, this research has centered on approaches that use a great deal of a priori information about the structure of the data. In this work, we apply statistical pattern recognition concepts to the problem of recursive nonparametric pattern recognition in dynamic environments.
- Statistics and Probability