[原创]Elsevier出品《Data Mining:Practical Machine Learning Tools and Techniques》

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

Paperback: 560 pages

Publisher: Morgan Kaufmann; 2 edition (June 8, 2005)

Language: English

ISBN: 0120884070

Review
"I was a big fan of the first edition and I'm excited about this new edition."
--Peter Norvig, Director of Search Quality, Google, Inc.

"This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate.

If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start."
--From the foreword by Jim Gray, Microsoft Research

Book Description
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

+ Authors, Ian Witten and Eibe Frank, recipients of the 2005 ACM SIGKDD Service Award.
+ Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods;
+ Performance improvement techniques that work by transforming the input or output;
+ Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface.

资料难得，象征性收5金钱

288517.pdf (7.77 MB, 需要: 5 个论坛币)

2009-1-24 11:29:00 上传

[原创]Elsevier出品《Data Mining:Practical Machine Learning Tools and Techniques》
需要: 5 个论坛币  [购买]

[此贴子已经被作者于2009-4-22 10:06:01编辑过]

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏2 回帖

关键词：Data Mining Techniques Practical Technique elsevier Mining Data Learning machine elsevier

部分目录：

• ch1 machine learning? where to use?application example.

 more examples of fielded applications

• ch2  input :concept instance attribute 输入

sparse data,string and date attribute

• ch3 output--knowledge representation different representation to different algorithms 输出

interactive decision tree

• ch4 basic methods of machine learning 算法基本思想

multinominal Bayes models for document classification,logistic regression

• ch5 performence evaluation

Kappa statistic for measuring the  success of a predictor   ；cost-sensitive learning bulid a cost-sensitive model  ;cost curves

• ch6 machine learning algorithms

NN ; Bayesin network classifiers;heuristics used in the successful RIPPER rule learner;model tree  to rules for numeric prediction ; apply locally weighted regression to classification problems;X-mean clustering algorithm

• ch7 practical topics 属性选择、离散化

new attribute selection schemes such as race search and
the use of support vector machines and new methods for combining models
such as additive regression, additive logistic regression, logistic model trees, and
option trees;LogitBoost ; useful transformations (principal components analysis and transformations for text mining and time series); using unlabeled
data to improve classification( co-training and co-EM methods).

 

好书，多谢了

多谢

好书，顶....

以下是引用kgbmca在2009-2-19 22:26:00的发言：
好书，顶....

多谢支持

的确是好书..

太感谢了，，，，

好书，多谢了,我正需要此书！！！

好书啊，支持下