First Advisor

Melanie Mitchell

Date of Publication

Winter 2-20-2014

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Computer Science

Department

Computer Science

Language

English

Subjects

Computer vision, Neural networks (Computer science), Machine learning

DOI

10.15760/etd.1664

Physical Description

1 online resource (xx, 131 pages)

Abstract

I conduct a study of learning in HMAX-like models, which are hierarchical models of visual processing in biological vision systems. Such models compute a new representation for an image based on the similarity of image sub-parts to a number of specific patterns, called prototypes. Despite being a central piece of the overall model, the issue of choosing the best prototypes for a given task is still an open problem. I study this problem, and consider the best way to increase task performance while decreasing the computational costs of the model. This work broadens our understanding of HMAX and related hierarchical models as tools for theoretical neuroscience, while simultaneously increasing the utility of such models as applied computer vision systems.

Rights

In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

Persistent Identifier

http://archives.pdx.edu/ds/psu/11083

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