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DC Field | Value | Language |
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dc.date.accessioned | 2021-04-07T06:01:48Z | - |
dc.date.available | 2021-04-07T06:01:48Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Muscat Rodo, M. (2018). Statistics of support vector machines with applications (Bachelor's dissertation). | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/73045 | - |
dc.description | B.SC.(HONS)STATS.&OP.RESEARCH | en_GB |
dc.description.abstract | The main goal of this dissertation is to attempt to identify whether or not medical breast images contain cancer. This is carried out using the Support Vector Machine, which is a supervised learning algorithm that fits a maximal margin hyperplane on a set of labelled training data. This algorithm is also carried out in conjunction with MayKay’s Evidence Frameworks, which uses Bayesian techniques in order to optimize any unknown parameters which are required for the learning algorithm. We also delve into the origins of this learning algorithm, its foundation and any key results in Stastical Learning Theory. The data used was obtained in part from the Digital Database for Screening Mammography and from the UCI Machine Learning Repository - where a select number of Haralick attributes were extracted from the dataset. A 5-Fold Cross Validation technique was also carried out during the research, in order to validate the results obtained. | en_GB |
dc.language.iso | en | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Breast -- Radiography | en_GB |
dc.subject | Support vector machines | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Bayesian statistical decision theory | en_GB |
dc.title | Statistics of support vector machines with applications | en_GB |
dc.type | bachelorThesis | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.publisher.institution | University of Malta | en_GB |
dc.publisher.department | Faculty of Science. Department of Statistics and Operations Research | en_GB |
dc.description.reviewed | N/A | en_GB |
dc.contributor.creator | Muscat Rodo, Malcolm (2018) | - |
Appears in Collections: | Dissertations - FacSci - 2018 Dissertations - FacSciSOR - 2018 |
Files in This Item:
File | Description | Size | Format | |
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18BSCMSOR003.pdf Restricted Access | 103.59 MB | Adobe PDF | View/Open Request a copy |
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