不再引起兴趣

For example,basic tools from calculus such as Fermat theorem,Rolle theorem and the intermediate value theorem may not necessarily hold and it is difficult to find a universal program for simulation in a model with various timescales,which attract attention of great deal researchers.In this PhD thesis,we first consider classification schemes for positive solutions of the first and second order dynamic systems.

在探讨测度链上的动力方程的动力学行为时人们所熟悉的基本工具诸如Fermat定理，Rolle定理以及介值定理等不再成立，同时很难找到适应不同测度链的模拟程序，这些在给测度链理论研究带来诸多困难的同时，也更引起了广大学者的兴趣。

The first few jokes intrigued us indeed, but the later ones palled on us.

开头的几个笑话的确引起了我们的兴趣，可是后来的几个就不再吸引我们了。

They wished themselves back again in the water, and after a month had passed they said it was much more beautiful down below, and pleasanter to be at home.

可是现在她们都已经是大女孩子了，可以随便浮到她们喜欢去的地方去了，这些东西也就不再能太引起她们的兴趣。

They wished themselves back again in the water, and after a month had passed they said it was much more beautiful down below, and pleasanter to be at home.

可是现在呢，她们已经是大女孩子了，可以随便浮近她们喜欢去的地方，因此这些东西就不再太引起她们的兴趣了。

In a database the concept of an example might change along with time which is known as concept drift When the concept drift the classification model built by use of old data is unsuitable for classify new data Therefore concept drift has become a hot issue in data mining in recent years Although many algorithms had been proposed to resolve this problem they can not provide users with the reason of concept drift However a user might be very interested in such rules For example doctors want to find what makes disease change; researchers want to know the reason of the variety of the weather; and decision makers would like to understand why a customer's shopping habit change In this thesis we propose a Concept Drift Rule mining Tree called CDR-Tree to solve this problem CDR-Tree can not only find the rule of concept drift also build the prediction model for both old and new data at the same time

无论在大型资料库或现实生活中，同一资料样本的概念有可能会随著时间的递移而改变，也就是产生所谓的概念漂移。当样本发生概念漂移时，由旧有资料所建构的分类模组将不再适用於预测新获得的资料，因此，近年来概念漂移已成为资料探勘中一项热门的研究议题。虽然已有？多学者提出不同的技术来解决概念漂移的问题，但是这些方法都是利用修正或重建的方式来产生适合新资料的预测模组，并无法提供造成概念漂移的原因。然而对使用者而言，其感兴趣的可能正是这些引起概念漂移的规则，如医生可能想了解引起疾病变化的主因、学者会想要知道气候转变的规则、或是决策者想找出顾客购物习惯改变的因素等。因此，本论文提出概念漂移规则探勘树( Concept Drift Rule mining Tree )，简称CDR-Tree，来解决这个问题。CDR-Tree不但能探测出造成概念漂移的主要原因，亦能同时建立新旧资料的预测模组以供决策者运用使用。

Means the parent directory, so this command means to execute "toolchain.sh," which is in the current directory.

代表父目录，所以这个命令就是执行当前目录下的"toolchain.sh"。

Yes,In fact,I'm on our city ream.

是的。事实上我是我们市队的。