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dc.contributor.authorZibamanzar-Mofrad, Tanaby
dc.date.accessioned2012-04-03T16:57:35Z
dc.date.available2012-11-12T16:38:47Z
dc.date.issued2012-04-03
dc.identifier.urihttp://hdl.handle.net/10464/3957
dc.description.abstractThe main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.en_US
dc.subjectPattern recognition systemsen_US
dc.subjectAlgorithmsen_US
dc.titleComparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithmsen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
dc.embargo.terms4 monthsen_US
refterms.dateFOA2021-08-08T01:23:33Z


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