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dc.contributor.advisorAnderson, Charles W.
dc.contributor.authorYeluri, Sri Sagar Abhishek
dc.contributor.committeememberBeveridge, Ross
dc.contributor.committeememberHess, Ann
dc.date.accessioned2020-09-07T10:08:39Z
dc.date.available2020-09-07T10:08:39Z
dc.date.issued2020
dc.description2020 Summer.
dc.descriptionIncludes bibliographical references.
dc.description.abstractIn a world where Machine Learning Algorithms in the field of Image Processing is being developed at a rapid pace, a developer needs to have a better insight into all the algorithms to choose one among them for their application. When an algorithm is published, the developers of the algorithm compare their algorithm with already available well-performing algorithms and claim their algorithm outperforms all or the majority of other algorithms in terms of accuracy. However, adaptability is a very important aspect of Machine Learning which is usually not mentioned in their papers. Adaptability is the ability of a Machine Learning algorithm to work reliably in the real world, despite the change in the environmental factors in comparison to the environment in which data used for training is recorded. A machine learning algorithm that can give good results only on the dataset has no practical applications. In real life, the application of the algorithm increases only when it is more adaptable in nature. A few other aspects that are important in choosing the right algorithm for an application are consistency, time and resource utilization and the availability of human intervention. A person choosing amongst a list of algorithms for an application will be able to make a wise decision if given additional information, as each application varies from one another and needs a different set of characteristics of an algorithm for it to be well received. We have implemented and compared three Machine Learning algorithms used in image processing, on two different datasets and compare the results. We observe that certain algorithms, even though better than others in terms of accuracy on paper, fall behind when tested in real-world datasets. We put forward a few suggestions that if followed will simplify the selection of an algorithm for a specific purpose.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierYeluri_colostate_0053N_16173.pdf
dc.identifier.urihttps://hdl.handle.net/10217/212032
dc.languageEnglish
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020- CSU Theses and Dissertations
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rightsCopyright of original work is retained by the authors.
dc.rights.licenseThis article is open access and distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
dc.rights.licenseThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 United States License.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectmachine learning
dc.subjectSiamese-like neural networks
dc.subjectneural networks
dc.subjectdeep learning
dc.titleClassification using out of sample testing of neural networks and Siamese-like neural network for handwritten characters
dc.typeText
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated (https://rightsstatements.org/vocab/CNE/1.0/). Please refer to the organization that has made the Item available for more information.
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)


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