Browsing by Author "Valipour, Mehrdad"

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    Theories and Methodologies for Cognitive Machine Learning based on Denotational Mathematics
    (2018-06-22) Valipour, Mehrdad; Wang, Yingxu; Gavrilova, Marina L.; Yanushkevich, Svetlana N.; Chen, Zhangxing; Chen, Liang
    Learning 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.