也分析比较了其他一些常用的剖分决策函数。
Moreover, other commonly used split decision functions are analyzed.
然后以实例对上述量价决策函数进行了实际验证。
Then, volume-price decision function above-mentioned is verificated in fact.
同时，递增式决策函数生成算法从根本上解决了多类决策的递增式学习问题。
At the same time, the question of learning about multi-classes is solved in essence by using the incremental generating decision function.
首先对数据进行预处理，得到各点的风险隶属度，并将其引入决策函数的生成过程。
The risk membership to each input points is confirmed on the base of processing input data, and then is leaded into the reasoning process of the decision function.
在线加工时，实时提取加工工件的特征向量并与各分类器进行对比，根据决策函数值即可识别该工件的类型。
During identification stage, characteristic vector of workpiece under machining should be extracted in real time and then compared with each classifier.
识别完成最后的分类，这个过程将前面提取出来的特征矢量用分类器进行分类，通过决策函数得到最后的分类结果。
Recognition completes the classification which classifies the above-mentioned character vector by classifier which gets the final result by decision-making function.
利用支持向量决策函数排序法(SVDFRM)，通过支持决策向量函数得到网络行为的特征贡献率并提取网络行为的重要特征。
A support vector decision function ranking method (SVDFRM) is used to calculate the contribution of network behaviors features, and then important network behaviors features are extracted.
利用支持向量决策函数排序法(SVDFRM)，通过支持决策向量函数得到网络行为的特征贡献率并提取网络行为的重要特征。
A support vector decision function ranking method (SVDFRM) is used to calculate the contribution of network behaviors features, and then important network behaviors features are extracted.
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