Accelerating classifier training using AdaBoost within cascades of boosted ensembles : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealand

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dc.contributor.authorSusnjak, Teo
dc.date.accessioned2009-09-29T20:21:25Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2009-09-29T20:21:25Z
dc.date.issued2009
dc.description.abstractThis thesis seeks to address current problems encountered when training classifiers within the framework of cascades of boosted ensembles (CoBE). At present, a signifi- cant challenge facing this framework are inordinate classifier training runtimes. In some cases, it can take days or weeks (Viola and Jones, 2004; Verschae et al., 2008) to train a classifier. The protracted training runtimes are an obstacle to the wider use of this framework (Brubaker et al., 2006). They also hinder the process of producing effective object detection applications and make the testing of new theories and algorithms, as well as verifications of others research, a considerable challenge (McCane and Novins, 2003). An additional shortcoming of the CoBE framework is its limited ability to train clas- sifiers incrementally. Presently, the most reliable method of integrating new dataset in- formation into an existing classifier, is to re-train a classifier from beginning using the combined new and old datasets. This process is inefficient. It lacks scalability and dis- cards valuable information learned in previous training. To deal with these challenges, this thesis extends on the research by Barczak et al. (2008), and presents alternative CoBE frameworks for training classifiers. The alterna- tive frameworks reduce training runtimes by an order of magnitude over common CoBE frameworks and introduce additional tractability to the process. They achieve this, while preserving the generalization ability of their classifiers. This research also introduces a new framework for incrementally training CoBE clas- sifiers and shows how this can be done without re-training classifiers from beginning. However, the incremental framework for CoBEs has some limitations. Although it is able to improve the positive detection rates of existing classifiers, currently it is unable to lower their false detection rates.en_US
dc.identifier.urihttp://hdl.handle.net/10179/1002
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectCoBE frameworken_US
dc.subjectClassifiersen_US
dc.subjectClassification algorithmsen_US
dc.subjectMachine learningen_US
dc.subject.otherFields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processing::280212 Neural networks, genetic algorithms and fuzzy logicen_US
dc.titleAccelerating classifier training using AdaBoost within cascades of boosted ensembles : a thesis presented in partial fulfillment of the requirements for the degree of Master of Science in Computer Sciences at Massey University, Auckland, New Zealanden_US
dc.typeThesisen_US
massey.contributor.authorSusnjak, Teo
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M. Sc.)en_US
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