Showing 1 - 20 results of 40 for search '((statistical sharing) OR (statistical learning)) theory', query time: 0.13s Refine Results
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    Refining learning models in grammatical inference by Wang, Xiangrui

    Published 2008
    “…Grammatical inference is a branch of computational learning theory that attacks the problem of learning grammatical models from string samples. …”
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    Thesis
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    Topics in Bayesian machine learning for finance by Spears, T

    Published 2024
    “…We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory. …”
    Thesis
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    Theoretical study of spermatozoa sorting by dielectrophoresis or magnetophoresis with supervised learning by Koh, James Boon Yong

    Published 2019
    “…The hydrodynamic force acting on the sperm is computed using Resistive Force Theory as well as Slender Body Theory, and the resulting velocity is compared qualitatively and quantitatively. …”
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    Thesis
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    Integrated user-interface acceptance model for e-learning system by Ramadiani, -

    Published 2014
    “…As a learning process, e-learning is aimed to achieve learning objectives, through which, the education is expected to become more accessible, cheaper, more fun, and easier to share and to learn. …”
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    Thesis
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    Advances in Sparse and Low Rank Matrix Optimization for Machine Learning Applications by Johnson, Nicholas André G.

    Published 2024
    “…This thesis advances both the theory and application of sparse and low rank matrix optimization, focusing on problems that arise in statistics and machine learning. …”
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    Thesis
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    A reformulation of additive models by Wozniakowski, Alex

    Published 2022
    “…Additive models and their fitting algorithms play a pivotal role in the history and development of applied mathematics, machine learning, statistics, and science. Yet, the traditional methodology neglects the means to explicitly incorporate prior knowledge into the fit of an additive model, which is of great practical importance in regression tasks, especially in the low training example regime, where entirely data-driven learning is not always feasible. …”
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    Thesis-Doctor of Philosophy