Titlebook: Improved Classification Rates for Localized Algorithms under Margin Conditions; Ingrid Karin Blaschzyk Book 2020 Springer Fachmedien Wiesb

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atency, consuming energy and improving privacy. As the most important technique of AI, Deep Neural Networks (DNN) has been widely used in various fields. And for those DNN based tasks, a new computing scheme named DNN model partition can further reduce the execution time. This computing scheme parti

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Discussion,of the input space, which splits the input space in a part in which cells are located close to the decision boundary and in a part in which the cells are sufficiently bounded away from it. This separation was described by a parameter . > 0 and we examined the excess risks on both parts separately.

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978-3-658-29590-5Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

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Ingrid Karin BlaschzykStudy in the field of natural sciences.Study in the field of statistical learning theory

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Histogram Rule: Oracle Inequality and Learning Rates,alysis of localized SVMs in Chapter 4 and highlights the main concepts that lead together with a certain splitting procedure to improved error bounds and learning rates, see Sections 3.1 and 3.2. We derive rates under margin conditions and compare them with other known rates in Section 3.3.

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Discussion,of the input space, which splits the input space in a part in which cells are located close to the decision boundary and in a part in which the cells are sufficiently bounded away from it. This separation was described by a parameter . > 0 and we examined the excess risks on both parts separately.

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