该算法的分割时间随温度系数的减小而增加，而温度系数的减小造成分割类数增多所导致的像素点重复计算问题，可通过并行计算来改进。
The segmentation time is increased with the reducing temperature due to the augment of the cluster's number. The problem of the pixel repeated computing can be solved with parallel computation.
分析了遗传算法中重复计算的可能性，在程序实现中避免了重复计算。
It can be indicated that there is possibility of reduplicate calculation of fitness by analysing the process of GA.
本文根据图像中曲线跟踪的特点，改进了步进方格算法，减少了其中的重复计算。
The marching square algorithm is often used in the front tracing, but the algorithm has some redundancy in the calculation.
文中讨论基于脉冲重复间隔(pri)的脉冲分选算法，深入分析了基于直方图统计算法和PRI变换算法的优缺点，并对算法进行计算机仿真。
In this paper, methods of sorting of radar pulses based on PRI are discussed. The histogram statistics algorithms and PRI transfer algorithm are analyzed and simulated.
该算法基于重复利用核心模块的方式完成波前斜率计算，利用矩阵与向量相乘的可分解性完成波前复原计算。
The algorithm calculated the wave front slope by a reusing core module manner and complemented the wave front reconstruction with the decomposition of matrix-vector multiplication.
本文对于矩形区域上某一内点为奇点的奇异积分的近似计算给出了优化中心数值算法，它在迭代计算过程中避免了函数值的重复计算。
This paper presents an optimum numerical algorithm of center rule for the approximate evaluation of singular integrals over rectangular domains with a inner singularity.
这种内外分层结构的迭代方式降低了算法设计的难度，减少了不必要的重复计算，提高了计算效率。
This inside and outside iterative way reduces the algorithm design difficulty, and nonessential double counting, raises the counting yield.
采用查询个体库的方法，可以避免相同个体的适应度函数的重复计算，节省了适应度评估的时间，提高了遗传算法的性能。
By inquiring the individuals base it can be evitable to re-calculate the fitness of same individuals. So the time of fitness evaluation is decreased and the performance of GA is improved.
霍红卫，白帆，一种具有精确边界的重复体识别算法，计算机学报， 2008，31（2）：214-219。
Hongwei Huo, Fan Bai, An Algorithm for Identification of Repeats with Accurate Boundaries, Chinese Journal of Computers, 2008, 31(2):214-219.
SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。
When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.
SVM分类算法在不同类别样本数目不均衡的情况下，训练时的分类错误倾向于样本数目小的类别。样本集中出现重复样本时作为新样本重新计算，增加了算法的训练时间。
When training sets with uneven class sizes are used, the classification error based on C-Support Vector Machine is undesirably biased towards the class with fewer samples in the training set.
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