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本文来自: 人大经济论坛 详细出处参考：http://www.pinggu.org/bbs/viewth ... amp;from^^uid=1076046

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本文来自: 人大经济论坛 详细出处参考：http://www.pinggu.org/bbs/viewth ... amp;from^^uid=1076046

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【英文篇名】	Induction of hybrid decision tree based on post-discretization strategy
【作者英文名】	[url=]WANG Limin * and YUAN Senmiao（College of Computer Science and Technology[/url]; [url=]Jilin University[/url]; [url=]Changchun 130012[/url]; [url=]China）[/url];
【文献出处】	Progress in Natural Science, 自然科学进展(英文版), 编辑部邮箱 2004年 06期   期刊荣誉：ASPT来源刊  CJFD收录刊
【英文关键词】	[url=]machine learning[/url]; [url=]hybrid decision tree[/url]; [url=]Naive Bayes.[/url];
【摘要】	Decisiontree basedmethodsofsupervisedlearn ingrepresentoneofthemost popularapproacheswithintheAIfieldfordealingwithclassificationproblems.Theyhavebeenwidelyusedforyearsinmanydomainssuchaspatternrecognition ,datamin ing ,signalprocessing ,etc .Butstandarddecisiontreelearningalgorithmscanhandlediscreteattributeson ly[1] .Learningdecisiontreefromdataconsistingofcontinuousanddiscretevariablesisakeyissueinma chinelearning .Thedecisiontreelearningalgorithmsproposedbeforecommonlyapplydiscretizationofcontinuousattr...
【英文摘要】	By redefining test selection measure,we propose in this paper a new algorithm,Flexible NBTree,which induces a hybrid of decision tree and Naive Bayes. Flexible NBTree mitigates the negative effect of information loss on test selection by applying post-discretization strategy: at each internal node in the tree,we first select the test which is the most useful for improving classification accuracy,then apply discretization of continuous tests. The finial decision tree nodes contain univariate splits as regula...
【基金】	SupportedbytheNationalNaturalScienceFoundationofChina (GrantNo .60 2 75 0 2 6)
【DOI】	cnki:ISSN:1002-0071.0.2004-06-012

lianghaibing 发表于 2009-12-7 18:21

【英文篇名】	Induction of hybrid decision tree based on post-discretization strategy
【作者英文名】	[url=]WANG Limin * and YUAN Senmiao（College of Computer Science and Technology[/url]; [url=]Jilin University[/url]; [url=]Changchun 130012[/url]; [url=]China）[/url];
【文献出处】	Progress in Natural Science, 自然科学进展(英文版), 编辑部邮箱 2004年 06期   期刊荣誉：ASPT来源刊  CJFD收录刊
【英文关键词】	[url=]machine learning[/url]; [url=]hybrid decision tree[/url]; [url=]Naive Bayes.[/url];
【摘要】	Decisiontree basedmethodsofsupervisedlearn ingrepresentoneofthemost popularapproacheswithintheAIfieldfordealingwithclassificationproblems.Theyhavebeenwidelyusedforyearsinmanydomainssuchaspatternrecognition ,datamin ing ,signalprocessing ,etc .Butstandarddecisiontreelearningalgorithmscanhandlediscreteattributeson ly[1] .Learningdecisiontreefromdataconsistingofcontinuousanddiscretevariablesisakeyissueinma chinelearning .Thedecisiontreelearningalgorithmsproposedbeforecommonlyapplydiscretizationofcontinuousattr...
【英文摘要】	By redefining test selection measure,we propose in this paper a new algorithm,Flexible NBTree,which induces a hybrid of decision tree and Naive Bayes. Flexible NBTree mitigates the negative effect of information loss on test selection by applying post-discretization strategy: at each internal node in the tree,we first select the test which is the most useful for improving classification accuracy,then apply discretization of continuous tests. The finial decision tree nodes contain univariate splits as regula...
【基金】	SupportedbytheNationalNaturalScienceFoundationofChina (GrantNo .60 2 75 0 2 6)
【DOI】	cnki:ISSN:1002-0071.0.2004-06-012

谢谢，我想要的是书。您提供的文献内容也不是我需要的。

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Book Name: Risk and Decision Analysis in Projects

Publisher: Project Management Institute | 2001 | PDF | 259 pages | ISBN: 1880410281 | 6.7Mb

Description:
Is there anything more important to the success of a project than making good decisions? This skill is certainly at or near the top of the list. Yet, few of us have had formal training in decision making. Decision analysis is the discipline that helps people choose wisely under conditions of uncertainty. This book introduces risk and decision analysis applied to project management. Probability is the language of uncertainty. Fortunately, a few basic concepts in probability and statistics go a long way toward making better decisions. The evaluation calculations are straightforward, and many everyday problems can be solved with a handheld calculator. Schuyler also explains and demystifies key concepts and techniques, including expected value, optimal decision policy, decision trees, the value of information, Monte Carlo simulation, probabilistic techniques, modeling techniques, judgments and biases, utility and multi-criteria decisions, and stochastic variance.