Nearest-Neighbor Methods in Learning and Vision 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Shakhnarovich, Gregory (EDT)/ Darrell, Trevor (EDT)/ Indyk, Piotr (EDT) 作者

Mit Pr

译者

2006-3 出版日期

280 页数

$ 50.85 价格

HRD

丛书系列

9780262195478 图书编码

Nearest-Neighbor Methods in Learning and Vision 在线电子书图书标签: 机器学习研究

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Nearest-Neighbor Methods in Learning and Vision 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Nearest-Neighbor Methods in Learning and Vision 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

Nearest-Neighbor Methods in Learning and Vision 在线电子书用户评价

评分☆☆☆☆☆

其实LSH不是一个特别简单的问题...

评分☆☆☆☆☆

不期而遇的一本书，最后两章正是我需要的

评分☆☆☆☆☆

其实LSH不是一个特别简单的问题...

评分☆☆☆☆☆

不期而遇的一本书，最后两章正是我需要的

评分☆☆☆☆☆

其实LSH不是一个特别简单的问题...

Nearest-Neighbor Methods in Learning and Vision 在线电子书著者简介

Nearest-Neighbor Methods in Learning and Vision 在线电子书图书目录

Nearest-Neighbor Methods in Learning and Vision 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Nearest-Neighbor Methods in Learning and Vision 在线电子书图书描述

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.

Nearest-Neighbor Methods in Learning and Vision 在线电子书下载 mobi epub pdf txt 在线电子书下载

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