Fast Kernel Expansions with Applications to CV and DL. Part 1b

I. de Zarzà

ISBN 10: 620392539X / ISBN 13: 9786203925395

Verlag: LAP LAMBERT Academic Publishing Jun 2021, 2021

Beschreibung

Neuware -The scope of the manuscript is to give a review of kernel expansions, FOURIER features and fast numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in large-scale datasets. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. Applications to Computer Vision (CV) and Deep Learning (DL) are enclosed with practical hints on the topic. Specifically, we give a primer on facial recognition and a foundation for the use of Vision in Robotics.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch.

Bestandsnummer des Verkäufers 9786203925395

Bibliografische Details

• Titel: Fast Kernel Expansions with Applications to CV and DL. Part 1b
• Verlag: LAP LAMBERT Academic Publishing Jun 2021
• Erscheinungsjahr: 2021
• Sprache: Englisch
• ISBN-10: 620392539X
• ISBN-13: 9786203925395
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 5 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 5 mm
• Gewicht: 131 Gramm

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Inhaltsangabe

The scope of the manuscript is to give a review of kernel expansions, FOURIER features and fast numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in large-scale datasets. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. Applications to Computer Vision (CV) and Deep Learning (DL) are enclosed with practical hints on the topic. Specifically, we give a primer on facial recognition and a foundation for the use of Vision in Robotics. <div><p style="text-align: justify;">The scope of the manuscript is to give a review of kernel expansions, FOURIER features and fast numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in large-scale datasets. It is well-known that kernel methods as originally proposed are computational costly for big data, we explain here the theory needed to enable the use of non-linear features in log-linear time. This approximation is based on FOURIER features by the use of the Walsh Hadamard. A SIMD implementation of the algorithm is described. Applications to Computer Vision (CV) and Deep Learning (DL) are enclosed with practical hints on the topic. Specifically, we give a primer on facial recognition and a foundation for the use of Vision in Robotics. </p></div>