至此设计出了一套完整的针对形态学骨架的剪枝算法。
Thus a complete pruning algorithm for the morphological skeleton is achieved.
初始过程的逆模型，建立了基于最小二乘支持向量机，并通过剪枝算法将支持向量的数量减少。
The initial inverse model of process is built based on least squares support vector machine, and the Numbers of support vector is reduced through pruning algorithm.
此外，程序员们也提出了很多算法和技巧来减少节点数目，比如阿尔法 贝塔剪枝算法（Alpha-Beta pruning），Negascout搜索算法以及MTD（全称是：Memory enhanced Test Driver，即记忆增强测试驱动，译者注）方法。
In addition, programmers have come up with all sorts of algorithms and tricks such as Alpha-Beta pruning, Negascout, and MTD(f) in order to lessen the number of nodes the computer must examine.
采用剪枝技术来消除构造过程中存在的冗余内涵比较，给出约束概念格渐进式构造算法PCCL。
An incremental construction algorithm named PCCL of the constrained concept lattice was presented by using pruning technology that eliminated the redundant information in the construction process.
算法采用了深度优先挖掘策略，并将基于前缀序列格的深度优先遍历与两种高效的剪枝策略相结合。
The search strategy of our algorithm integrates a depth-first traversal of the prefix sequence lattice with two effective pruning mechanisms.
在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Besides, in this algorithm, we have used some strategy of pruning, which make the algorithm's time complexity more lower.
而且，该算法采用了有效的迭代剪枝技术，大大压缩了候选模式的数量，降低了通信代价。
Moreover, this algorithm adopts an effective iterative and pruning strategy that could compress the scale of candidate patterns and reduce the communication cost.
此外，对近似的样本神经元进行合并，使用剪枝操作删除含坏数据的样本神经元，从而保证了算法的健壮性，并且精炼了网络结构。
Furthermore, similar example neurons are combined and neurons with bad data are cut to maintain robustness of the algorithm and simpleness of the network architecture.
提出了一种增强预测范围聚集查询epra算法，采用更精确的剪枝搜索准则，减少了查询所需要访问的节点代价。
Also developed for the PRA tree is an enhanced predictive range aggregate (EPRA) query algorithm which USES a more precise branch and bound searching strategy, reducing the disk I?
该算法构造了前缀树表示序列模式，并用广度剪枝和深度剪枝维护该前缀树的结构。
It constructs a prefix tree to represent the sequence patterns, and continuously maintains the tree structure by using width pruning and depth pruning.
这避免了后剪枝策略所需的高昂代价，减少了扫描磁盘数据的次数和大量的CPU时间，进一步提高了算法的效率。
This avoid high cost by using post-pruning measure which require many times for scanning disk data and amount of CPU time. So we gain a high efficiency.
研究表明，该方法不仅算法简单、只需扫描一次数据库，而且还具有动态剪枝、不保存中间候选项和节省大量内存空间等优点。
Studies show that the method is not only simple that needs to scan the database only once, but also has the virtues such as dynamic pruning, without saving mid items and save lots of memories.
介绍了算法中如何处理高分枝属性、数值属性和缺失数据及剪枝等关键环节。
Some key aspects about algorithm are introduced here, such as how to deal with high-branching and numeric attributes, missing values as well as how to prune.
算法采用的预测剪枝策略减少了挖掘的次数，采用的求取公共交集的方式保证了挖掘结果的完整性。
This algorithm reduced the number of mining through early prediction before mining. The application of a method to get the public intersection sets could obtain a complete result.
利用该方法对基本ID 3决策树算法进行了改进。分析和实验表明，与先剪枝方法相比，该方法能进一步减小决策树的规模和训练时间。
By using the method to improve the ID3 algorithm, experiments show that the algorithm generates smaller decision tree and USES less training time than the algorithm using pre-pruning method.
针对运动图上的路径搜索，提出了基于路径曲线所夹面积的目标函数并改进了分段搜索算法和剪枝策略。
Concerning path search in the motion graph, this paper used the area between two curves as the target function and improved the strategy of incremental search and the strategy of branch and bound.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
在时间序列的模式表示的基础上挖掘其频繁子模式，可以大大提高挖掘的效率和准确性，达到事半功倍的效果。在该算法中，还使用了一定的剪枝策略，使得算法的时间复杂度进一步降低。
Mining Time Series Frequent Sub-pattern based on Pattern Representation can enormously increase the efficiency and veracity of mining, and get twice the result with half the effort.
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