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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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发表于 2025-3-21 20:09:21 | 显示全部楼层 |阅读模式
书目名称Composing Fisher Kernels from Deep Neural Models
副标题A Practitioner‘s App
编辑Tayyaba Azim,Sarah Ahmed
视频video
概述Presents a step-by-step approach to deriving a kernel from any probabilistic model belonging to the family of deep networks.Demonstrates the use of feature compression and selection techniques for red
丛书名称SpringerBriefs in Computer Science
图书封面Titlebook: Composing Fisher Kernels from Deep Neural Models; A Practitioner‘s App Tayyaba Azim,Sarah Ahmed Book 2018 The Author(s), under exclusive li
描述This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fisher vectors using feature selection and compression techniques. Feature selection and feature compression are two of the most popular off-the-shelf methods for reducing data’s high-dimensional memory footprint and thus making it suitable for large-scale visual retrieval and classification. Kernel methods long remained the .de facto. standard for solving large-scale object classification tasks using low-level features, until the revival of deep models in 2006. Later, they made a comeback with improved Fisher vectors in 2010. However, their supremacy was always challenged by various versions of deep models, now considered to be the state of the art for solving various machine learning and computer vision tasks. Although the two research paradigms differ significantly, the excellent performance of Fisher kernels on the Image Net large-scale object classification dataset has caught the attention of numerous kernel practitioners, and many have drawn pa
出版日期Book 2018
关键词Deep Models; Fisher Vectors; Large Scale Information Retrieval; Feature Compression Techniques; Feature
版次1
doihttps://doi.org/10.1007/978-3-319-98524-4
isbn_softcover978-3-319-98523-7
isbn_ebook978-3-319-98524-4Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive licence to Springer Nature Switzerland AG 2018
The information of publication is updating

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发表于 2025-3-22 00:09:01 | 显示全部楼层
Regressions- und Korrelationsanalyse,n of kernel methods and the heuristics and methods that have helped kernel methods evolve over the past many years for solving the challenges faced by current machine learning practitioners and applied scientists.
发表于 2025-3-22 04:03:58 | 显示全部楼层
Kernel Based Learning: A Pragmatic Approach in the Face of New Challenges,n of kernel methods and the heuristics and methods that have helped kernel methods evolve over the past many years for solving the challenges faced by current machine learning practitioners and applied scientists.
发表于 2025-3-22 06:20:46 | 显示全部楼层
2191-5768 use of feature compression and selection techniques for redThis book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fish
发表于 2025-3-22 12:25:45 | 显示全部楼层
发表于 2025-3-22 15:34:04 | 显示全部楼层
Fundamentals of Fisher Kernels, was filled by Tommy Jaakola through the introduction of . kernels in 1998 and since then it has played a key role in solving problems from computational biology, computer vision and machine learning. We introduce this concept here and show how to compute Fisher vector encodings from deep models using a toy example in MATLAB.
发表于 2025-3-22 20:22:32 | 显示全部楼层
发表于 2025-3-23 00:05:24 | 显示全部楼层
发表于 2025-3-23 03:28:29 | 显示全部楼层
Die Technik der praktischen Statistik,chniques discussed in this book. We have shared comparative analysis of the resources in tabular form so that users could pick the tools keeping in view their programming expertise, software/hardware dependencies and productivity goals.
发表于 2025-3-23 07:17:30 | 显示全部楼层
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