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Title: decision tree for uncertain data project using algorithm ppt
Page Link: decision tree for uncertain data project using algorithm ppt -
Posted By: sabarish
Created at: Thursday 17th of August 2017 04:44:05 AM
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Classification is a classical problem in machine learning
and data mining. Given a set of training data tuples, each
having a class label and being represented by a feature vector,
the task is to algorithmically build a model that predicts
the class label of an unseen test tuple based on the tuple s
feature vector. One of the most popular classification models
is the decision tree model. Decisions trees are popular because
they are practical and easy to understand. Rules can also be
extracted from decision trees easily. Many algorithms ....etc

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Title: privacy preserving decision tree learning using unrealized data sets ppt
Page Link: privacy preserving decision tree learning using unrealized data sets ppt -
Posted By: OJO FELICIA
Created at: Thursday 17th of August 2017 08:23:30 AM
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Title: future enhancements to decision tree for uncertain data
Page Link: future enhancements to decision tree for uncertain data -
Posted By: Tushar
Created at: Thursday 05th of October 2017 03:56:29 AM
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I am doing the project on decision tree for uncertain data.can you please tell me the future enhancements to this paper. ....etc

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Title: algorithms used for decision trees for uncertain data ppt
Page Link: algorithms used for decision trees for uncertain data ppt -
Posted By: lipi
Created at: Thursday 17th of August 2017 07:59:47 AM
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what are the distributed based algorithms used in the decision ....etc

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Title: privacy preserving decision tree learning using unrealized data sets 2012
Page Link: privacy preserving decision tree learning using unrealized data sets 2012 -
Posted By: binithekkethil
Created at: Thursday 05th of October 2017 04:39:39 AM
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Title: Generalization and decision tree induction efficientclassification in data mining
Page Link: Generalization and decision tree induction efficientclassification in data mining -
Posted By: brijesh_donda007
Created at: Thursday 17th of August 2017 06:20:36 AM
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Abstract
Efficiency and scalability are fundamental issues concerning data mining in large databases. Although classification has been studied extensively, few of the known methods take serious consideration of efficient induction in large databases and the analysis of data at multiple abstraction levels. The paper addresses the efficiency and scalability issues by proposing a data classification method which integrates attribute oriented induction, relevance analysis, and the induction of decision trees. Such an integration leads to efficient ....etc

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Title: decision tree for uncertain data java source code download
Page Link: decision tree for uncertain data java source code download -
Posted By: sruthy
Created at: Thursday 05th of October 2017 05:27:58 AM
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Title: Privacy preserving decision tree for uncertain data in dotnet
Page Link: Privacy preserving decision tree for uncertain data in dotnet -
Posted By: shantanu
Created at: Thursday 05th of October 2017 04:02:53 AM
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Title: Privacy Preserving Decision Tree Learning Using Unrealized Data Sets
Page Link: Privacy Preserving Decision Tree Learning Using Unrealized Data Sets -
Posted By: master
Created at: Thursday 05th of October 2017 04:40:32 AM
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Abstract Privacy preservation is important for machine learning and data mining, but measures designed to protect private information often result in a trade-off: reduced utility of the training samples. This paper introduces a privacy preserving approach that can be applied to decision tree learning, without concomitant loss of accuracy. It describes an approach to the preservation of the privacy of collected data samples in cases where information from the sample database has been partially lost. This approach converts the original sample da ....etc

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Title: decision tree for uncertain data ppt download
Page Link: decision tree for uncertain data ppt download -
Posted By: kadi
Created at: Thursday 17th of August 2017 05:43:03 AM
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Abstract

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting unc ....etc

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