Titlebook: Composing Fisher Kernels from Deep Neural Models; A Practitioner‘s App Tayyaba Azim,Sarah Ahmed Book 2018 The Author(s), under exclusive li

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Regressions- und Korrelationsanalyse,ls on the topic by Sch?lkopf and Smola (Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press (2002), [.]), Shawe-Taylor, Cristianini (Kernel methods for pattern analysis. Cambridge University Press (2004), [.]), Kung (Kernel methods and machine learning

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https://doi.org/10.1007/978-3-322-94465-8nificant benefit of Fisher vectors for classification and retrieval problems, they suffer from the problem of high dimensionality giving rise to computational and storage overhead for large scale learning problems. This chapter provides guidelines for tackling this issue by either deploying feature

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Die Technik der praktischen Statistik,ng a variety of deep learning models, kernel functions, Fisher vector encodings and feature condensation techniques. Not only can the users benefit from the open source codes, a rich collection of benchmark data sets and tutorials can provide them all the details to get hands on experience of the te

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978-3-319-98523-7The Author(s), under exclusive licence to Springer Nature Switzerland AG 2018

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