Salience-affected neural networks

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dc.contributor.advisorTapson, Jonathanen_ZA
dc.contributor.advisorEllis, GFRen_ZA
dc.contributor.authorRemmelzwaal, Leendert Amanien_ZA
dc.date.accessioned2015-01-13T03:48:50Z
dc.date.available2015-01-13T03:48:50Z
dc.date.issued2009en_ZA
dc.descriptionIncludes abstract.en_ZA
dc.descriptionIncludes bibliographical references (leaves 46-49).en_ZA
dc.description.abstractIn this research, the salience of an entity refers to its state or quality of standing out, or receiving increased attention, relative to neighboring entities. By neighbouring entities we refer to both spatial (i.e. similar visual objects) and temporal (i.e. related concepts). In this research we model the effect of non-local connections using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying the node’s thresholds, depending on the activation level of the node. Each node produces a nodal reverse salience signal during testing (a measure of the threshold bias for the individual node). The reverse salience signal is defined as the summation of the nodal reverse salience signals observed at each node.en_ZA
dc.identifier.apacitationRemmelzwaal, L. A. (2009). <i>Salience-affected neural networks</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/12111en_ZA
dc.identifier.chicagocitationRemmelzwaal, Leendert Amani. <i>"Salience-affected neural networks."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2009. http://hdl.handle.net/11427/12111en_ZA
dc.identifier.citationRemmelzwaal, L. 2009. Salience-affected neural networks. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Remmelzwaal, Leendert Amani AB - In this research, the salience of an entity refers to its state or quality of standing out, or receiving increased attention, relative to neighboring entities. By neighbouring entities we refer to both spatial (i.e. similar visual objects) and temporal (i.e. related concepts). In this research we model the effect of non-local connections using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying the node’s thresholds, depending on the activation level of the node. Each node produces a nodal reverse salience signal during testing (a measure of the threshold bias for the individual node). The reverse salience signal is defined as the summation of the nodal reverse salience signals observed at each node. DA - 2009 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2009 T1 - Salience-affected neural networks TI - Salience-affected neural networks UR - http://hdl.handle.net/11427/12111 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/12111
dc.identifier.vancouvercitationRemmelzwaal LA. Salience-affected neural networks. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2009 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/12111en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleSalience-affected neural networksen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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