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    Object Classification using L-Fuzzy Concept Analysis

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    Author
    Addison, George Tsekpetse
    Keyword
    formal concept analysis
    fuzzy concept analysis
    machine learning
    artificial intelligence
    object classification
    
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    URI
    http://hdl.handle.net/10464/14884
    Abstract
    Object classification and processing have become a coordinated piece of modern industrial manufacturing systems, generally utilized in a manual or computerized inspection process. Vagueness is a common issue related to object classification and analysis such as the ambiguity in input data, the overlapping boundaries among the classes or regions, and the indefiniteness in defining or extracting features and relations among them. The main purpose of this thesis is to construct, define, and implement an abstract algebraic framework for L-fuzzy relations to represent the uncertainties involved at every stage of the object classification. This is done to handle the proposed vagueness that is found in the process of object classification such as retaining information as much as possible from the original data for making decisions at the highest level making the ultimate output or result of the associated system with least uncertainty.
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