Study on Electoral College based Deep Learning and Its Applications

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On the theoretical front, we will study the stability of the Electoral College-based Deep Learning. Its shallowest form, Electoral College, has been used for many years in US presidential elections, and applied in areas such as image processing, pattern recognition, information retrieval, and other political elections. Our model will be the first in Artificial Intelligence research that illustrates the stability of a class of Deep Learning with respect to region/prefecture size, number of layers, and overlapping rates of neighboring regions in each layer. Our theory will guide applications on how to choose an adequate number of layers, an adequate region size as well as adequate overlapping rates of neighboring regions for each layer, for using Electoral College-based Deep Learning to improve the performance of pattern recognition and information retrieval approaches. Developments made on the theoretical front will be applied in two important fields: (1) A face image set-based surveillance system will be developed. This system will combine proven active infrared illumination technology for pupil location with a mathematical approach developed in this research program to match unknown faces to known faces on watch lists. The system is expected to be a significant improvement, when compared to any appearance-based face recognition system, in recognition rate vs. false acceptance rate, and in being disguise-proof. (2) An intelligent textual information retrieval system will be developed. This system will employ a novel approach, where a document is treated as a physical object which can be described from different "view angles", for document representation so that each document can be divided into overlapped blocks. A vector space model will be employed as the basic strategy for matching a query with "block" in a document, and the Electoral College-based Deep Learning will be employed in making retrieval decisions. The system is expected to have much better performance in precision vs. recall compared to other approaches. These systems can be further developed for use in national security systems and internet intelligent document retrieval systems respectively.