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dc.contributor Ouyang, Youwen en
dc.contributor.advisor Wu, Shaun-Inn en
dc.contributor.author Lucian, Christopher en
dc.date.accessioned 2015-05-06T22:10:55Z en
dc.date.available 2015-05-06T22:10:55Z en
dc.date.issued 2015-05-06 en
dc.date.submitted 2015-05-06 en
dc.identifier.uri http://hdl.handle.net/10211.3/138828 en
dc.description.abstract We investigate the impact of Differential Evolution Binary Particle Swarm Optimization (DEBPSO) on the configuration of learning models used to classify the shape prototypes generated by hierarchical neural-network models of vision. Visual cortex inspired models of vision build a dictionary of shape prototypes represented as a feature vector which is then used for classification of difficult feature invariant recognition problems. We show that high performance on invariant object-recognition tasks can be improved upon by configuring the learning models used in the classification of the shape prototypes utilizing DEBPSO. When regarding imprinted and random prototypes with the evolutionary algorithm configured learning models we show that a larger improvement can be achieved on the imprinted prototypes than the random prototypes. These results show a better way of classifying the shape prototypes used by the hierarchical models of vision and that imprinted prototypes do contain more useful information than random prototypes that was previously underutilized by the un-configured learning models used in prior research. en
dc.description.sponsorship Computer Science en
dc.language.iso en_US en
dc.subject Computer Vision en
dc.subject Machine Learning en
dc.subject Object Recognition en
dc.subject Computer Science en
dc.title Optimizing and Evaluating Classifiers of a Visual Cortex based Hierarchical Model Using Differential Evolution Binary Particle Swarm Optimization en
dc.type Thesis en


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