Theories and Methodologies for Cognitive Machine Learning based on Denotational Mathematics

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dc.contributor.advisorWang, Yingxu
dc.contributor.authorValipour, Mehrdad
dc.contributor.committeememberGavrilova, Marina L.
dc.contributor.committeememberYanushkevich, Svetlana N.
dc.contributor.committeememberChen, Zhangxing
dc.contributor.committeememberChen, Liang
dc.date2018-11
dc.date.accessioned2018-07-05T20:02:32Z
dc.date.available2018-07-05T20:02:32Z
dc.date.issued2018-06-22
dc.description.abstractLearning is a cognitive process of knowledge and behavior acquisition for both humans and machines. Cognitive machine learning systems are increasingly demanded in modern knowledge-based industry, society, and everyday lives. This study on theories and applications of cognitive machine learning based on denotational mathematics is designed to develop methodologies, algorithms, and their implementations for machine enabled knowledge learning at the conceptual, phrasal, and sentence levels via cognitive computing technologies. The main objectives of this work are: a) To develop a cognitive and mathematics-based machine learning theory for knowledge acquisition and semantic manipulations; b) To enable machines to autonomously learn and understand semantics expressed in natural languages underpinned by unsupervised cognitive computing algorithms; and c) To design and implement a brain-inspired cognitive learning engine for inductively learning from the level of formal concepts to those of phrases and sentences. The thesis is embodied by three novel and autonomous machine knowledge learning algorithms underpinned by Wang’s denotational mathematics. In this research, properties of formal concepts and mathematical rules of concept algebra are formally studied. A method for building quantitative semantic hierarchies of formal concepts is implemented by cognitive machine learning. Theories and mathematical models for an unsupervised algorithm of phrase learning are developed based on rigorous concept comprehensions by cognitive machine learning. A machine knowledge learning system for sentence syntactic analysis and semantic synthesis is developed and implemented by novel cognitive computing technologies. This thesis does not only present a set of basic studies on machine learning challenges in the sixth category of knowledge learning and semantic comprehension, but also implement efficient cognitive machine learning systems mimicking human learning. This research will enable a wide range of industrial applications such as cognitive robotics, natural language comprehension systems, personal leaning assistants, cognitive search engines, and language translators.en_US
dc.identifier.citationValipour, M. (2018). Theories and Methodologies for Cognitive Machine Learning based on Denotational Mathematics (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32272en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32272
dc.identifier.urihttp://hdl.handle.net/1880/107050
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.subjectMachine Learning
dc.subjectComputational Linguistics
dc.subjectCognitive Science
dc.subject.classificationLinguisticsen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationComputer Scienceen_US
dc.titleTheories and Methodologies for Cognitive Machine Learning based on Denotational Mathematics
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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