| 1. | Studies on decisiontree classifying method in dataming
数据挖掘中决策树分类方法研究 |
| 2. | Decision tree classification algorithm based on bayesian method
基于贝叶斯方法的决策树分类算法 |
| 3. | In this paper , the decision tree classify algorithm is choosed as the emphasis
选取决策树分类算法为研究重点。 |
| 4. | This paper is a study on decision tree classification algorithms , which mainly includes two parts
本文主要对决策树分类算法展开研究，主要包含两个内容： 1 |
| 5. | The traditional decision tree category methods ( such as : id3 , c4 . 5 ) are effective on small data sets
摘要传统的决策树分类方法（如id3和c4 . 5 ）对于相对小的数据集是很有效的。 |
| 6. | So bp neural network is used to classify the stored food insects primarily . the result shows our method is effective
实验表明，利用bp神经网络分类器作初级分类器的树分类结构进行分类，是一种行之有效的方法。 |
| 7. | This new idea can be applied for any other algorithm . thus it produces a new way for improving classifying algorithm accuracy
这种方法也可以应用于其它的决策树构造算法中，为提高决策树分类算法的准确性提供了新的途径。 |
| 8. | Many different techniques have been proposed for classification , including statistical approaches , neural networks , decision tree algorithm and rough sets
现有数据分类方法有统计方法、决策树分类方法、神经网络方法、粗集法等。 |
| 9. | Secondly , decision tree classification model and logistic regression model are performed to rock mass quality assessment , based on sas / enterprise miner
应用sas enterpriseminer系统的决策树分类算法和logistic回归算法进行岩体的质量分级评价。 |
| 10. | In this paper , based on the comprehension of the current research situation , we mainly discussed the problem how to adapt the decision tree in common use to the large scale dataset
针对大数据量、多属性值的情况，对决策树分类器所需属性信息的求解提出了新的改进算法。 |
