【电子书免费下载】《数据挖掘：实用机器学习工具与技术》(英文第2版)PDF下载

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【电子书免费下载】《数据挖掘：实用机器学习工具与技术》(英文第2版)PDF下载

作者: Ian H. Witten / Eibe Frank
出版社: Morgan Kaufmann
副标题: Practical Machine Learning Tools andTechniques, Second Edition
出版年: 2005-6-22

内容简介

As with any burgeoning technology that enjoys commercial attention,the use of data mining is surrounded by a great deal of hype. Exaggeratedreports tell of secrets that can be uncovered by setting algorithms loose onoceans of data. But there is no magic in machine learning, no hidden power, noalchemy. Instead there is an identifiable body of practical techniques that canextract useful information from raw data. This book describes these techniquesand shows how they work. The book is a major revision of the first edition thatappeared in 1999. While the basic core remains the same, it has been updated toreflect the changes that have taken place over five years, and now has nearlydouble the references. The highlights for the new edition include thirty newtechnique sections; an enhanced Weka machine learning workbench, which nowfeatures an interactive interface; comprehensive information on neuralnetworks; a new section on Bayesian networks; plus much more; algorithmicmethods at the heart of successful data mining-including tried and truetechniques as well as leading edge methods; performance improvement techniquesthat work by transforming the input or output; and, downloadable Weka, acollection of machine learning algorithms for data mining tasks, includingtools for data pre-processing, classification, regression, clustering,association rules, and visualization-in a new, interactive interface.

作者介绍

Jiawei Han(韩家炜)，是伊利诺伊大学厄巴纳-尚佩恩分校计算机科学系的Bliss教授。他因知识发现和数据挖掘研究方面的贡献而获得许多奖励，包括ACM SIGKDD创新奖(2004)、IEEE计算机学会技术成就奖(2005)和IEEE W.Wallace McDowell奖(2009)。他是ACM和IEEE会士。他还担任《ACM Transactions on Knowledge Discovery from Data》的执行主编(2006—2011)和许多杂志的编委，包括《IEEE Transactions onKnowledge and Data Engineering》和《Data Mining KnowledgeDiscovery》。

拥有加拿大康考迪亚大学计算机科学硕士学位，现在加拿大西蒙弗雷泽大学从事博士后研究工作。

目录
PartI Machine learning tools and techniques
  1 What’s it all about?
  2 Input: Concepts, instances, andattributes
  3 Output: Knowledge representation
  4 Algorithms: The basic methods
  5 Credibility: Evaluating what’s beenlearned
  6 Implementations: Real machine learningschemes
  7 Transformations: Engineering the inputand output
  8 Moving on: Extensions and applications
PartII The Weka machine learning workbench
  9 Introduction to Weka
  10 The Explorer
  11 The Knowledge Flow interface
  12 The Experimenter
  13 The command-line interface
  14 Embedded machine learning
  15 Writing new learning schemes
References
Index
Aboutthe authors

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Data Mining_Practical Machine Learning Tools and Techniques (3rd Edition)_Ian H. Witten 2011

Data Mining_Practical Machine Learning Tools and Techniques (4th Edition)_Ian H. Witten 2016

虎虎856 发表于 2018-2-3 15:03
【电子书免费下载】《数据挖掘：实用机器学习技术》(英文第2版)PDF下载

作者: Ian H. Witten / Eibe Fr ...

谢谢分享！

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虎虎856 发表于 2018-2-3 15:03
【电子书免费下载】《数据挖掘：实用机器学习技术》(英文第2版)PDF下载

作者: Ian H. Witten / Eibe Fr ...

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