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    Constructivism Learning: A Learning Paradigm for Transparent Predictive Analytics

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    Li_ku_0099D_15772_DATA_1.pdf (650.9Kb)
    Issue Date
    2018-05-31
    Author
    Li, Xiaoli
    Publisher
    University of Kansas
    Format
    166 pages
    Type
    Dissertation
    Degree Level
    Ph.D.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
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    Abstract
    Aiming to achieve the learning capabilities possessed by intelligent beings, especially human, researchers in machine learning field have the long-standing tradition of bor- rowing ideas from human learning, such as reinforcement learning, active learning, and curriculum learning. Motivated by a philosophical theory called "constructivism", in this work, we propose a new machine learning paradigm, constructivism learning. The constructivism theory has had wide-ranging impact on various human learning theories about how human acquire knowledge. To adapt this human learning theory to the context of machine learning, we first studied how to improve leaning perfor- mance by exploring inductive bias or prior knowledge from multiple learning tasks with multiple data sources, that is multi-task multi-view learning, both in offline and lifelong setting. Then we formalized a Bayesian nonparametric approach using se- quential Dirichlet Process Mixture Models to support constructivism learning. To fur- ther exploit constructivism learning, we also developed a constructivism deep learning method utilizing Uniform Process Mixture Models.
    URI
    http://hdl.handle.net/1808/27594
    Collections
    • Engineering Dissertations and Theses [1055]
    • Dissertations [4473]

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    KU Libraries
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    785-864-8983

    KU Libraries
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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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