| 1. | Bayesian - network based regression tree learning algorithm
基于贝叶斯网络的回归树学习算法 |
| 2. | Secret is a new algorithm for scalable linear regression trees
Secret是一个关于可伸缩线形回归树的算法。 |
| 3. | Improves on the classification and regression trees technology , increases it ' s classification precision
对分类回归树数据挖掘技术进行了改进，使之具有更高的分类精度。 |
| 4. | The thesis combines generalized computing theory with classification and regression trees technology , makes the great theory innovation
本文把广义计算理论和数据挖掘技术相结合，具有很强的理论创新意义。 |
| 5. | Combines multi - rules neural network with classification and regression trees technology based on generalized computing theory , implements the abnormal customers recognition system
基于广义计算思想，把多准则神经网络和分类回归树技术相结合，实现异动客浙江大学硕士学位沦义缀户识别系统。 |
| 6. | The first part of this thesis describes the theory of rbf neural networks . the input space is thus divided into hyperrectangles organized into a regression tree ( binary tree ) by recursively partition the input space in two
第一部分从rbf网络出发，通过递归分割将输入空间划分为两部分，从而将输入空间变成一个用超矩形构成的回归树(二叉树) 。 |
| 7. | Based on the generalized computing theory , the thesis combines multi - rules neural network with a kind of decision tree - classification and regression trees . further more , we put forward a new kind of abnormal customers recognition model
为进行客户关系管理，本文基于广义计算思想，将多准则神经网络和一种决策树? ?分类回归树相结合，提出了一种新的异动客户识别模型。 |
| 8. | Chapter three is about credit risk measurement methodology such as zeta model , credit scoring - model , classification & regression tree , csfp model and credit metrics , the latest of which can measure the credit risk of abs / mbs dynamically
包括zeta法、资信评估模型、分类和回归树、 cspp开发的信用风险附加模型；动态评估资产证券化的信用风险技术? ?信用风险计量法。 |
| 9. | You can use multiple algorithms within one solution to perform separate tasks , for example by using a regression tree algorithm to obtain financial forecasting information , and a rule - based algorithm to perform a market basket analysis
可以在一个解决方案中使用多个算法来执行不同的任务，例如，使用回归树算法来获取财务预测信息，使用基于规则的算法来执行市场篮分析。 |
| 10. | The model can improve classification precision and recognition efficiency effectively , make full use of the advantages of multi - rules neural network and classification and regression trees , and make up their respective disadvantages at a certain extent
该模型能够有效提高分类精度和识别效率，充分利用多准则神经网络和分类回归树各自的优点，一定程度上避免各自的缺陷。 |
