Ensemble Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Zhi-Hua Zhou 作者

Chapman and Hall/CRC

译者

2012-6-6 出版日期

236 页数

USD 87.95 价格

Hardcover

丛书系列

9781439830031 图书编码

Ensemble Methods 在线电子书图书标签: 机器学习集成学习 MachineLearning 周志华数据挖掘计算机数学计算机视觉

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Ensemble Methods 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Ensemble Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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比想象中薄，简介但内容充实，关键的点有说到。主要关注的是基础部分，以及Class-Imbalance Learning那一块，AdaCost， SMOTEBoost， Easyensemble，BalanceCascade等。有些基础的预备知识没有遗漏，恰恰这些对于新手帮助很大，也便于形成概观，这点做得很好。总之，不错的综述。

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灰常好

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13年已读。

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周老师大牛呀

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Ensemble Methods 在线电子书著者简介

Zhi-Hua Zhou is a professor in the Department of Computer Science and Technology and the National Key Laboratory for Novel Software Technology at Nanjing University. Dr. Zhou is the founding steering committee co-chair of ACML and associate editor-in-chief, associate editor, and editorial board member of numerous journals. He has published extensively in top-tier journals, chaired many conferences, and won six international journal/conference/competition awards. His research interests encompass the areas of machine learning, data mining, pattern recognition, and multimedia information retrieval.

Ensemble Methods 在线电子书图书目录

Ensemble Methods 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Ensemble Methods 在线电子书图书描述

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.

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