Description: 入侵检测中的数据挖掘方法研究.pdf-A Research into Data Mining Technology in Intrusion Detection. file type pdf Platform: |
Size: 613376 |
Author:赵艳 |
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Description: C4.5算法有如下优点：产生的分类规则易于理解，准确率较高。其缺点是：在构造树的过程中，需要对数据集进行多次的顺序扫描和排序，因而导致算法的低效。此外，C4.5只适合于能够驻留于内存的数据集，当训练集大得无法在内存容纳时程序无法运行。-C4.5 algorithm has the following advantages: the classification rules easier to understand, accurate and a higher rate. Its shortcomings are as follows: in the tree structure, the need for a number of data sets the order of scanning and sorting, thus leading to inefficient algorithms. In addition, C4.5 can only be applied to the presence of a data set in memory, when the training set too great to accommodate in memory when the program can not run. Platform: |
Size: 1024 |
Author:xinyuanwo |
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Description: c4.5 关于决策树decision tree的matlab实现程序 -c4.5 decision tree decision tree on the realization of the matlab program Platform: |
Size: 4096 |
Author:凌风 |
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Description: 这是一个分类和回归树算法，它提供一种通用框架将各种各样不同的判定树实例化。-This is a classification and regression tree algorithm, which provides a common framework a wide variety of different decision tree instantiation. Platform: |
Size: 1024 |
Author:肖箫 |
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Description: The matlab code implements the ensemble of decision tree classifiers proposed in: "L. Nanni and A. Lumini, Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification, Expert Systems With Applications doi:10.1016/j.eswa.2009.02.072 " Platform: |
Size: 1024 |
Author:loris nanni |
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Description: 决策树算法的matlab实现，主要适用的是id3
算法思想-Decision Tree Algorithm to achieve the matlab main id3 algorithm is applicable to thinking Platform: |
Size: 6144 |
Author:fj |
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Description: 对已有图像进行区域识别，画出决策树，并在测试图像上进行测试。-The image of the existing regional recognition, draw the decision tree, and the test images for testing. Platform: |
Size: 5120 |
Author:chang |
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Description: 实现ID3算法，在结果中以树表示出来。决策树是对数据进行分类，以此达到预测的目的。该决策树方法先根据训练集数据形成决策树，如果该树不能对所有对象给出正确的分类，那么选择一些例外加入到训练集数据中，重复该过程一直到形成正确的决策集。-ID3 algorithm to achieve, in the results that come out to the tree. Decision tree is to classify the data, thus achieving the purpose of prediction. The decision tree training set of data according to the formation of the first decision tree, if the tree can not give the correct classification of all objects, then select a number of exceptions to the training set data, repeat the process until the correct decision set. Platform: |
Size: 2048 |
Author:王剑亭 |
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Description: his algorithm was proposed by Quinlan (1993). The C4.5 algorithm generates a classification-decision tree for the given data-set by recursive partitioning of data. The decision is grown using Depth-first strategy. The algorithm considers all the possible tests that can split the data set and selects a test that gives the best information gain. For each discrete attribute, one test with outcomes as many as the number of distinct values of the attribute is considered. For each continuous attribute, binary tests involving every distinct values of the attribute are considered. In order to gather the entropy gain of all these binary tests efficiently, the training data set belonging to the node in consideration is sorted for the values of the continuous attribute and the entropy gains of the binary cut based on each distinct values are calculated in one scan of the sorted data. This process is repeated for each continuous attributes. Platform: |
Size: 2048 |
Author:rajesh |
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Description: 为了准确地对监控场景中的运动目标进行语义上的分类，提出了一种基于聚类的核主成分分析梯度方向直方图和二又决策树支持向量机的运动目标分类算法。-In order to accurately monitor the movement of scene targets semantic classification, the clustering based on kernel principal component analysis of gradient direction histograms, and two and a support vector machine decision tree classification algorithm of moving objects. Platform: |
Size: 544768 |
Author:piano |
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Description: 本书论述在MATLAB环境下如何实现神经网络，包括了常用的神经网络及相关理论，如BP神经 网络、RBF神经网络、SVM、SOM神经网络、灰色神经网络、决策树、随机森林、小波神经网络、NARX神经网络等以及各种优化算法与神经网络的结合。(This book discusses how to realize neural network in MATLAB environment, including the commonly used neural network and related theory, combined with the BP neural network, RBF neural network, SVM, SOM neural network, grey neural network, decision tree, random forest, wavelet neural network and NARX neural network, optimization algorithm and neural network.) Platform: |
Size: 54043648 |
Author:三月M
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Search - decision tree in MATLAB