决策树

其中，有施教者的学习算法在多Agent系统中应用最为普遍，比如人工神经网络算法和决策树算法等。

In Multi-Agent Systems , supervised learning algorithms are widely used such as the Artificial Neuron Network and Decision Tree .

其二是基于分级聚类和决策树思想构建的多类SVM算法，介绍了算法的思想和具体实现，在小样本情况下对两种算法进行了应用。

Second is multi-class algorithm based on hierarchical clustering and decision tree .

一个基于决策树的中文Web文本挖掘系统

A Decision Tree-based Web Mining System for Chinese Pages

基于Web的自映射空间决策树方法研究

Decision-tree Induction from Self-map Space Based on Web

基于多个混合核函数的SVM决策树算法设计

Designing the algorithm of SVM decision tree based on many mixture of kernels

基于PSO改进决策树算法的研究

Research on Decision Tree Method Based on Improved PSO

基于逻辑描述的决策树算法及其Prolog实现

Decision Tree algorithm based on logical description and realization using Prolog

基于决策树的MPEG视频镜头分割算法

The MPEG Video Shot Detection Based on Decision Tree

客户端使用VCMFC6.0实现各种决策树算法，PB编程实现轮换的集成环境。

The client point employs VC MFC 6.0 to implement decision Tree algorithms and PB programming to implement the rotation integrated circumstance .

与此同时，本文探讨了数据挖掘技术的应用现状、决策树的生成算法和剪枝技术以及BP神经网络算法的相关知识。

At the same time , it discusses the present status of data mining application , the decision-tree generation algorithms , pruning techniques and back propagation neural network algorithm .

基于PIDC和二叉决策树SVM的人耳识别

Ear Recognition Based on PIDC and Binary Tree SVM Classification

并针对C4.5决策树算法实现提出了三种改进策略；

This paper puts forward three strategies to improve C4.5 algorithm .

基于ASTER数据遥感影像的决策树分类

Decision Tree Based on ASTER Image Classification and Its Application

一种基于锚文本和改进C4.5决策树算法的主题爬行方法

Focused crawling method based on improved C4.5 exploiting anchor text

实验结果表明，新方法在提高分类效率、保证分类精度的同时可以大大降低SVM决策树误差积累的影响。

Experiment results show that the proposed algorithm can improve recognition efficiency , control error accumulation and at the same time ensure recognition precision . 6 .

决策树和GIS图层叠置方法研究结果表明，土壤有机质始终占据决策树全部知识规则的第一次分支，起着重要的决定性作用，其次为速效磷和碱解氮；

The results based on decision-making tree and GIS overlay analysis show that the soil organic matter always holds the first place in all knowledge rules , P and N rank the second .

本文阐述了分布式关联规则算法（FDM）、分布式分类决策树算法（SPRINT）。

This pager analysis and compare , the distributed decision tree algorithms for classification and the distributed association rules algorithms .

本研究采用C4.5算法，计算土壤类型-环境因子和土壤有机质-环境因子之间的关系，分别得到它们的决策树模型。

The C4.5 algorithm was used to extract the relation of soil type-landscape and SOM-landscape .

比如，在决策树的学习算法中，使用SVM的最大间隔替代最小信息熵，作为启发式信息，这将大大改善决策树的泛化能力。

For example , the maximal margin of SVM is chosen as a heuristic strategy to substitute minimum entropy in decision tree learning algorithm , which can attain better generalization .

由于构建最优的模糊决策树已被证明是NP-hard问题，因此，一般采用启发式方法来构建模糊决策树。

However , constructing the optimal fuzzy decision tree has been proved to be NP-hard , a heuristic algorithm is necessary .

针对这些局限性，设计了由Logistic回归分析方法、关联规则、决策树和神经网络组成的联合数据挖掘系统。

Then this paper points out the defect of traditional financial warning methods and designs a united data mining system consisting of Logistic regression , associative rules , decision tree and neural network .

另外，为便于比较，在MATLAB环境下采用CAMM算法编写了一个决策树构造程序。

Moreover , the paper compiles a decision tree-making program by CAMM algorithm in MATLAB in order to compare with the clustering .

各个两分类SVM在决策树中的位置不同，其分类性能往往也会不同，越接近根节点的位置出现错分，其误差积累现象越严重。

The classification performance is often different when SVM is in different positions of the decision tree . Error accumulation is very serious when the mismatch happen in approach to the root node .

还引入了元挖掘算法Bagging，与决策树模型综合使用，进一步提高了预测效果。

In addition , a meta mining method , bagging , is introduced to be combined with decision tree to achieve even better prediction accuracy .

在Web挖掘的基本概念、基本应用和一般文本分类的基础上，本文从系统功能、模块设计和核心算法三方面详细介绍了一个具有自学习能力的基于决策树的中文Web文本挖掘系统。

We analyzed the principle and usage of the term Web mining , suggested three Web mining categories . Then we introduced a Web mining system for Chinese web pages , based on decision tree , and discussed its function , module design and core algorithms .

将改进的PSO引入到决策树建树方法中，并与传统的决策树方法及使用遗传算法改进后的树进行比较，验证了其优越性。

Building up decision tree by improved PSO , the paper gives the example to validate that the improved algorithm is better than the original decision tree method and by improved by GA.

UCI数据的实验结果和实际应用都表明，用多决策树进行模式递增学习的方法可以有效地提高识别系统的判决精度。

Experiments on UCI datasets and trials in real applications show that the proposed method can improve the accuracy of recognition effectively .

采用决策树ID3算法和支持向量分类算法，构建了提取次数的分类器；

Classifier taking extracting times as target attribute was constructed by decision tree ID3 and support vector classification algorithm .

首先用Fisher线性判别法对人脸图象进行特征抽取，压缩了图象的维数，再在特征空间中用SVM（supportVectorMachine）和决策树结合的方法设计人脸分类器。

First the Fisher discriminant method is used to extract the feature of human face and to reduce the dimensionality of the images . Then a classifier is designed on the feature space by using support vector machine and the decision tree .

决策树的学习算法，比如ID3算法，选用最小信息熵作为启发式信息。

Minimum entropy is chosen as a heuristic strategy in decision tree ( DT ) learning algorithm such as ID3 .