Condition Connector is renamed Decision node.
状态连接器(Condition Connector)重命名为决策节点(Decision Node)。
A Decision node is another type of node that supports custom implementations.
Decision节点是另一种节点，它支持自定义实现。
The extended AON (Activity on Node) graph is utilized to structurize the business process with decision nodes.
在经营过程中，存在许多决策节点，给出了扩展的AON图，结构化描述带有决策节点的经营过程。
A decision node holds the business rules (transition conditions) that are evaluated to decide what path in the process should be followed.
决策点遵循业务规则(转换条件)，使用这些业务规则来确定流程应当依照什么路线进行。
The join node waits for the completion of these two parallel routes before advancing to the second decision point.
连接点等待着两条平行路线都完成，然后进入第二个决策点。
A branch node identifies the decision points and is visually represented as a black box located on the associated arrow (transition).
分支节点识别决策点，并直观地表示为位于相关箭头(转换)上的黑框。
The style sheet compiler therefore constructs a decision tree which is used at run time to decide which template rule to apply to a given node.
因此样式表编译器构造了决策树，在运行时用它来决定将哪个模板规则应用于给定节点。
The business process can invoke the rule set as its decision service node via various bindings.
业务流程可以通过多种绑定作为决策服务节点调用规则集。
In reconstruction, the net's decision to flip a node is based on the sum of the products of the values of the other nodes and the weights between them.
在重构中，网络基于其他节点的值及它们之间的权重的乘积的和做出翻转一个节点的判定。
Immediately following the start node, a decision represents three alternative branches in the process flow that eventually lead to a join preceding Task 5.
在开始节点后，紧跟一个decision，它表示流程流中的三个可选分支，这些分支最终引向Task 5之前的join。
The user captured the two final events that the task triggers after successful execution in a decision with two outgoing branches that directly end in a stop node.
当在一个带有两个传出分支（这两个分支直接终止于某个停止节点）的decision 中成功执行之后，用户会捕获由任务触发的那两个最终事件。
The first cycle involves a backward connection that starts in the decision and leads back to the join following the start node.
在第一个循环中，有一个逆向连接从decision开始并引回位于开始节点之后的 join。
The third cycle starts in the fork preceding the end node, and leads back to the join following the decision.
第三个循环从结束节点之前的 fork中开始，并引回decision之后的 join。
Immediately following the start node, we see an inclusive decision with two branches.
我们看到，紧跟开始节点后面的是一个带有两个分支的包含性decision。
A Decision handler can be configured the same way as the Action handler when implementing Node functionality.
当实现Node功能时，可以像配置Action处理器一样配置Decision处理器。
The idea of designing tree and classifier designer are proposed to control the design process of decision tree and de-sign the classifier of the node of decision tree.
提出设计树和分类器设计器的概念，分别用于决策树设计过程的控制和决策树节点中的分类器的设计。
After pretreatment, the data were loaded in SQL2000 server database. We established the case mix of the inpatient using the grouping node selected by decision tree.
所有资料经预处理后，导入SQL2000数据库，利用其中的数据挖掘工具——微软决策树进行分类节点变量的选取，以此构建病例组合。
The existing majority of the decision tree inductions are based on a top-down greedy algorithm, which make a locally optimal decision at each node.
目前存在的决策树归纳算法大多数是基于自顶向下的贪婪算法，它在每个结点都执行一个局部最优决策。
The model created a series of subsystems from the known decision system at different reduced levels to form a reduced lattice, and then reasoned its own rule set at each node.
该模型从已知决策系统出发，建立了处于不同简化层次上的一系列子系统，并将其作为简约格中的节点，然后推导出每个节点的规则集。
This paper will explain the process of constructing the test node and propose a two-stage decision tree algorithm.
本文将从几何学角度说明构造测试节点的过程，提出了一种两阶段决策树的算法。
Obviously selected attribute of a node is important and related to quality of decision-tree.
在树的生成过程中，非叶结点相关属性的选择直接决定树的质量。
Obviously selected attribute of a node is important and related to quality of decision-tree.
在树的生成过程中，非叶结点相关属性的选择直接决定树的质量。
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