dc.contributor.authorNguwi, Yok Yen
dc.date.accessioned2011-03-22T01:16:08Z
dc.date.accessioned2017-07-23T08:29:13Z
dc.date.available2011-03-22T01:16:08Z
dc.date.available2017-07-23T08:29:13Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.citationNguwi, Y. Y. (2011). Self-organizing cortical processing with visual feature selection for pattern recognition. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/43537
dc.description.abstractPattern recognition has been studied extensively, and many algorithms have been established. It generally makes use of discriminant functions to learn the pattern in data. These discrimant functions are developed to be simplistic so as to warrant fast computations. In addition, simple evaluation functions are easier to learn because there are lesser parameters to estimate. However, this simplicity may not work well when new ‘pattern’ in data surfaces. Humans recognize an object or pattern from surrounding world in split second; however this involves many processing in the human visual system. Human gathers most of the sensory information through sight. Visual-perceptual processing covers approximately one-fourth of the cortex. Visual information processing is also the most complex, most studied sensory system of the brain. It is envisaged that if the visual cortex can process information in such a lightning speed, there should exist some combinations of feature selection and pattern classification which are close enough to provide such capability. The motivation behind the research of this thesis is to establish a computational framework that attempts to emulate the visual cortical processing in the human brain. The aim is to recognize a pattern in short computation time even when sparse data is presented.en_US
dc.format.extent196 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognitionen_US
dc.titleSelf-organizing cortical processing with visual feature selection for pattern recognitionen_US
dc.typeThesis
dc.contributor.researchCentre for Computational Intelligenceen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.contributor.supervisorCho Siu-Yeung Daviden_US
dc.description.degreeDOCTOR OF PHILOSOPHY (SCE)en_US
dc.identifier.doihttps://doi.org/10.32657/10356/43537


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