813,863 research outputs found

    Using Hybrid Effectively in Christian Higher Education

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    Hybrid is just one of a number of terms used for the convergence of face-to-face and online learning, At the University of Central Florida (UCF) they are called mixed mode courses, In the corporate world the most common language used for hybrid is blended learning, Blended learning, says Bob Mosher, is about using multiple learning modalities, which include, but are not limited to, the Web.7 The blended learning term is also being used more frequently within academic circles,8 Because of the inconsistency in how blended learning is employed, though, and because our goal is not to describe learning in general but to focus on individual courses, this article will use the term hybrid and will apply it more narrowly to mean a course in which face-to-face and online learning are integrated in such a way that the seat time of the course is reduced

    A Generalized Method for Integrating Rule-based Knowledge into Inductive Methods Through Virtual Sample Creation

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    Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for classification. Methods that use domain knowledge have been shown to perform better than inductive learners. However, there is no general method to include domain knowledge into all inductive learning algorithms as all hybrid methods are highly specialized for a particular algorithm. We present an algorithm that will take domain knowledge in the form of propositional rules, generate artificial examples from the rules and also remove instances likely to be flawed. This enriched dataset then can be used by any learning algorithm. Experimental results of different scenarios are shown that demonstrate this method to be more effective than simple inductive learning

    Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control

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    Today's fast linear algebra and numerical optimization tools have pushed the frontier of model predictive control (MPC) forward, to the efficient control of highly nonlinear and hybrid systems. The field of hybrid MPC has demonstrated that exact optimal control law can be computed, e.g., by mixed-integer programming (MIP) under piecewise-affine (PWA) system models. Despite the elegant theory, online solving hybrid MPC is still out of reach for many applications. We aim to speed up MIP by combining geometric insights from hybrid MPC, a simple-yet-effective learning algorithm, and MIP warm start techniques. Following a line of work in approximate explicit MPC, the proposed learning-control algorithm, LNMS, gains computational advantage over MIP at little cost and is straightforward for practitioners to implement
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