Fast Kernel Expansions with Applications to CV and DL. Part 1a : Carnegie Mellon. City University of Hong Kong

J. de Curtò

ISBN 10: 6203925381 / ISBN 13: 9786203925388

Published by LAP LAMBERT Academic Publishing, 2021

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. 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. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. 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. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.

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Bibliographic Details

• Title: Fast Kernel Expansions with Applications to CV and DL. Part 1a : Carnegie Mellon. City University of Hong Kong
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 6203925381
• ISBN 13: 9786203925388
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 149 grams

About this title

Synopsis

The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. 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. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.<div><p style="text-align: justify;">The main aim of the text is to give a review of fast kernel expansions, FOURIER features and rapid numerical code in statistical learning. For this purpose we introduce a library for approximating kernel expansions, which enables the use of kernel methods in datasets with a large number of samples. 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. The manuscript contains interesting applications to Computer Vision (CV) and Deep Learning (DL) which can serve as guideline for novel researchers in the topic. In particular we provide a primer on facial recognition and directives for the use of large-scale techniques of Vision in Robotics.</p></div>