Nearest-Neighbor Methods in Learning and Vision 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Nearest-Neighbor Methods in Learning and Vision

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Shakhnarovich, Gregory (EDT)/ Darrell, Trevor (EDT)/ Indyk, Piotr (EDT) 作者
Mit Pr
譯者
2006-3 出版日期
280 頁數
$ 50.85 價格
HRD
叢書系列
9780262195478 圖書編碼

Nearest-Neighbor Methods in Learning and Vision 在線電子書 圖書標籤: 機器學習  研究   


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發表於2024-11-27

Nearest-Neighbor Methods in Learning and Vision 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Nearest-Neighbor Methods in Learning and Vision 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Nearest-Neighbor Methods in Learning and Vision 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



Nearest-Neighbor Methods in Learning and Vision 在線電子書 用戶評價

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不期而遇的一本書,最後兩章正是我需要的

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其實LSH不是一個特彆簡單的問題...

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其實LSH不是一個特彆簡單的問題...

評分

不期而遇的一本書,最後兩章正是我需要的

評分

其實LSH不是一個特彆簡單的問題...

Nearest-Neighbor Methods in Learning and Vision 在線電子書 著者簡介


Nearest-Neighbor Methods in Learning and Vision 在線電子書 著者簡介


Nearest-Neighbor Methods in Learning and Vision 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Nearest-Neighbor Methods in Learning and Vision 在線電子書 圖書描述

Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.

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