Titlebook: Lightning Protection for People and Property; Marvin M. Frydenlund Book 1993 Van Nostrand Reinhold 1993 Potential.computer.design.educatio

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Marvin M. Frydenlund performace of our approach on the collected complex dataset. It contains various watermeter images in real scenario which has illumination changes, messy environment, half-digit and blurring. It is observed that the proposed algorithm outperforms existing methods. Our approach runs 10?fps with 96.1

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Marvin M. Frydenlundty of pruned classifiers among nearest neighbors of testing data points. By this way, each test data point can build a unique locally weighted classifier ensemble. Analysis of experimental results on several UCI data sets shows that the classification results of our method are better than other ense

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Marvin M. Frydenlundhance the ability for handling nonlinear problems, we also extend the proposed method to arbitrary kernel space. Extensive experimental results on several face recognition benchmark datasets demonstrate the superior performance of our proposed algorithm.

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Marvin M. Frydenlundhance the ability for handling nonlinear problems, we also extend the proposed method to arbitrary kernel space. Extensive experimental results on several face recognition benchmark datasets demonstrate the superior performance of our proposed algorithm.

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Marvin M. Frydenlundtion relies on naming services, the latter being an integral part of a network architecture. Revisiting the definitions of Shoch, Hauzeur, Saltzer and Karsten, we start by disambiguating the plethora of terms used in the naming literature—., ., ., ., ., ., ., ., and .. We present the definitions and

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