Theories and Experiments of Cognitive Knowledge Bases for Machine Learning

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dc.contributor.advisorWang, Yingxu
dc.contributor.authorZatarain Duran, Omar Ali
dc.contributor.committeememberGavrilova, Marina L.
dc.contributor.committeememberFapojuwo, Abraham Olatunji
dc.contributor.committeememberChen, Zhangxing
dc.contributor.committeememberBudin, Gerhard
dc.date2018-11
dc.date.accessioned2018-07-05T20:06:10Z
dc.date.available2018-07-05T20:06:10Z
dc.date.issued2018-06-26
dc.description.abstractThis thesis presents a framework of studies on theories, methodologies, algorithms, and experiments on cognitive knowledge bases (CKBs) for machine knowledge learning in cognitive computing and computational linguistics. CKB is both the results and the means of machine learning methodologies mimicking human learning and semantic comprehensions. Technologies for machine learning can be classified into six categories according to Dr. Y. Wang known as object identification, cluster classification, pattern recognition, functional regression, behavior generation, and knowledge acquisition. Most current machine learning techniques fall into the first five categories. However, the sixth category of knowledge learning as humans do has remained as a fundamental problem and challenge in machine learning, AI, and computational intelligence. A set of algorithms, tools, and experiments on machine knowledge learning is designed in order to demonstrate that cognitive machines may create their own concepts and CKBs through knowledge learning. The accuracy and cohesiveness of machine learnt results may outperform humans. This leads to the implementation of formal knowledge comprehension and quantitative semantic analyses by cognitive systems based on CKBs and machine semantic comprehensions. The theoretical framework and case studies derived from this research will impact the field of machine knowledge learning technologies and the development of novel cognitive systems. This research will enable industrial applications such as personal leaning assistants, cognitive search engines, and cognitive translators.en_US
dc.identifier.citationZatarain, O. A. (2018). Theories and Experiments of Cognitive Knowledge Bases for Machine Learning (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32273en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32273
dc.identifier.urihttp://hdl.handle.net/1880/107051
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectcognitive machine learning
dc.subjectdenotational mathematics
dc.subjectconcept elicitation
dc.subjectcognitive knowledge bases
dc.subjectcognitive robotics
dc.subjectcognitive computing
dc.subject.classificationLinguisticsen_US
dc.subject.classificationStatisticsen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.titleTheories and Experiments of Cognitive Knowledge Bases for Machine Learning
dc.typedoctoral thesis
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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