Description: 决策树学习的通用程序。可以根据自己的具体要求扩充功能-Decision Tree Learning common procedures. According to the specific requirements of their functional expansion Platform: |
Size: 3511 |
Author:代桂平 |
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Description: 一个模拟weka的系统，输入文件格式和weka的一样，实现决策树的分析以及通过数据挖掘整理规则集合，很值得新手学习-a simulation system, the importation of files and weka, the same realization of the decision tree analysis and data mining collated by the rules set, is worth learning newcomers Platform: |
Size: 37888 |
Author:郑磊 |
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Description: C4.5是决策树的经典算法
C4.5 归纳学习是完全自动的学
习算法，所需要做的是选取有用的特征，构建实例数据库供它学习-C4.5 decision tree is the classic C4.5 inductive learning algorithm is completely automatic learning algorithm, what needs to be done is to select useful features, build databases for its examples of learning Platform: |
Size: 1024 |
Author:唐宇 |
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Description: 一个由Mike Gashler完成的机器学习方面的includes neural net, naive bayesian classifier, decision tree, KNN, a genetic algorithm, and some manifold learning algorithms. -by Mike Gashler a complete machine learning includes the neur al net, naive bayesian classifier. decision tree, KNN, a genetic algorithm, manifold and some learning algorithms. Platform: |
Size: 1625088 |
Author:lyb |
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Description: 基于粗糙集理论的决策树预修剪学习算法研究,对决策树算法加以了改进．-Based on rough set theory learning algorithm for decision tree pre-pruning research on decision tree algorithm to be improved. Platform: |
Size: 561152 |
Author:laishuguang |
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Description: 机器学习中,很重要的一种技术是决策树,这是一个关于决策树技术的详细介绍-Machine learning, it is important and technology is a decision tree, this is a detail on the decision tree technology introduction Platform: |
Size: 1529856 |
Author:syscxl |
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Description: ID3决策树算法的JAVA实现：ID3算法是机器学习中的一种分类方法，本例子用java构建多叉树来实现id3算法。-ID3 Decision Tree Algorithm JAVA realize: ID3 machine learning algorithm is a classification method, the example of using java to build a multi-tree algorithm id3 realize. Platform: |
Size: 460800 |
Author:more |
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Description: /*
The sample demonstrates how to build a decision tree for classifying mushrooms.
It uses the sample base agaricus-lepiota.data from UCI Repository, here is the link:
Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998).
UCI Repository of machine learning databases
[http://www.ics.uci.edu/~mlearn/MLRepository.html].
Irvine, CA: University of California, Department of Information and Computer Science.
*/
// loads the mushroom database, which is a text file, containing
// one training sample per row, all the input variables and the output variable are categorical,
// the values are encoded by characters. Platform: |
Size: 4096 |
Author:tofighi |
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Description: Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods.-Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods. Platform: |
Size: 312320 |
Author:keerthi |
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Description: Decision tree learning algorithm has been successfully used in expert
systems in capturing knowledge. The main task performed in these systems is
using inductive methods to the given values of attributes of an unknown object
to determine appropriate classification according to decision tree rules. Platform: |
Size: 22528 |
Author:GasPro |
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Description: 基于决策树的增量学习算法相关文档，主要用于文本分类-Incremental learning algorithm based on decision tree document, mainly for text classification Platform: |
Size: 1107968 |
Author:liujingwen |
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Description: 这个是我自己整理的关于语音识别HMM模型训练部分的决策树相关学习总结-Decision tree learning summary of HMM model training for speech recognition Platform: |
Size: 1490944 |
Author:王小雷 |
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Description: 二叉决策树能很好地运行，适合初学者,希望能帮助大家更好地学习算法-Binary decision tree can run well, suitable for beginnersHope I can help you better learning algorithm
Platform: |
Size: 1024 |
Author:解俊雄 |
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Description: 一个决策树学习的matlab例程，里面带有配套调试图像，可以直接运行-decision tree learning a matlab routine, which contains a set of debugging images, you can run directly
Platform: |
Size: 275456 |
Author:897lp |
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