Generalization of hierarchical retinotopic networks using stochastic distortion models

The generalization of hierarchical retinotopic networks is modeled as a type of probability measure called "tail probability" with a stochastic distortion field. Learning in the network memorizes the exemplars in terms of the distribution. Generalization in a hierarchical retinotopic network is characterized by the probability measure of multilevel events and decision making at each abstraction level. The concept is applied to automatically generating a hierarchical retinotopic network during the leaning of exemplars. This approach is called Cresceptron and it has been tested on learning, recognizing and segmenting a variety of real-world objects based on their 2-D images.<
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