3,007,818 research outputs found

    LQG Online Learning

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    Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical Linear Quadratic Gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a non-trivial variation as it involves random matrices, are investigated. The obtained optimal solutions are compared with the Kalman-filter estimate of the parameter vector to be learned. It is shown that the proposed algorithm is less sensitive to outliers with respect to the Kalman estimate (thanks to the presence of the regularization term), thus providing smoother estimates with respect to time. The basic formulation of the proposed online-learning framework refers to a discrete-time setting with a finite learning horizon and a linear model. Various extensions are investigated, including the infinite learning horizon and, via the so-called "kernel trick", the case of nonlinear models

    A framework for developing and implementing an online learning community

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    Developing online learning communities is a promising pedagogical approach in online learning contexts for adult tertiary learners, but it is no easy task. Understanding how learning communities are formed and evaluating their efficacy in supporting learning involves a complex set of issues that have a bearing on the design and facilitation of successful online learning experiences. This paper describes the development of a framework for understanding and developing an online learning community for adult tertiary learners in a New Zealand tertiary institution. In accord with sociocultural views of learning and practices, the framework depicts learning as a mediated, situated, distributed, goal-directed, and participatory activity within a socially and culturally determined learning community. Evidence for the value of the framework is grounded in the findings of a case study of a semester-long fully online asynchronous graduate course. The framework informs our understanding of appropriate conditions for the development and conduct of online learning communities. Implications are presented for the design and facilitation of learning in such contexts
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