• Notice that the compile method includes the optimization specified by Step 2 of our algorithm.

    注意,compile方法包括算法的第2步所指定的优化过程。

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  • Hi, our algorithm knows your career dreams!

    嗨,我们的算法能预测到你的职业梦想!

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  • The experiment results show that our algorithm is feasible.

    实验结果表明所提出的算法是行之有效的。

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  • The experimental results given at the end of this paper demonstrate the effectiveness of our algorithm.

    最后给出的实验结果证明了本文方法的有效性。

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  • Simulation results show that our algorithm can avoid link congestion and reduce the call blocking probabilities.

    结果表明,该算法能较好地避免链路拥塞,同时具有较低的呼叫阻塞率。

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  • Experimental results indicate that our algorithm is effective.

    实验结果表明给出的算法是有效的。

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  • The simulated results indicate that our algorithm is feasible.

    模拟结果表明该算法是可行的。

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  • It represents the "strength" level of the word, figured out by our algorithm.

    它代表相应单词的“强度”等级,是根据我们的算法得出的。

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  • Experiments show that our algorithm enhance the perception of stereo display.

    实验表明,该算法的确改善了立体显示的效果。

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  • With lots of experiments, we proved the feasibility and high accuracy of our algorithm.

    通过大量的实验,本文证明了此算法的可行性和很好的准确性。

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  • The experiment results show that our algorithm is efficient enough for practical demands.

    实验结果表明,该算法效率高,合成效果达到了实用的要求。

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  • For hotspot traffic, about 17% savings in buffering resources can be achieved using our algorithm.

    在热点流量中,该算法可节省约17%的缓冲资源。

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  • Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering.

    在此,我们就这些方法与我们的算法——我们称之为商品到商品的协同过滤——进行对比。

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  • Theoretical analysis and experimental evaluations are also presented to show the efficiency of our algorithm.

    另外,理论分析和试验表明了此算法的有效性。

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  • Both computer simulation and real images test indicates that the result of our algorithm is stable and accurate.

    模拟实验和真实图象实验表明,本方法的定标结果稳定,精度较高。

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  • The result shows our algorithm can quickly generate high quality images better than general antialiasing algorithm.

    实验结果表明,新算法生成的图像比较细腻,实时性比较高,优于一般的反走样算法。

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  • The experiment results prove that our algorithm can meet the vision-based navigation's request for real-time and validity.

    实验结果表明,该算法计算量小、鲁棒性强,完全满足视觉导航中的实时性、准确性要求。

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  • The simulation results demonstrate our algorithm to be of high-resolution and high-precision with arbitrary array geometry.

    计算机模拟结果证实了该算法对不同阵列获取的信号的测频有效性并显示出良好的高分辨、高精度性能。

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  • This makes it possible for the extension of our algorithm to multiple-output combinational network and even sequential network.

    这为进一步将该算法拓展至实际的多输出组合网络乃至同步时序网络打下了基础。

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  • The results of our experiments show the feasibility of our algorithm to improve the accuracy of heights by airborne Lidar data.

    实验结果显示,本文所提出的方法对于改善空载光达的高程精度是可行的。

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  • Experimental results show that our algorithm has a better performance on network lifetime than the common clustering algorithms.

    实验结果表明,新算法较常见分簇算法更能够延长网络寿命。

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  • Experiments on UCI data sets show that compared with traditional methods our algorithm can get more accurate rules in a shorter time.

    UCI数据库上的实验结果表明,与传统方法相比该算法能够在更短的时间内得到分类精度更高的规则。

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  • The search strategy of our algorithm integrates a depth-first traversal of the prefix sequence lattice with two effective pruning mechanisms.

    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。

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  • Experiments show that our algorithm can achieve quite better fusion effect and is advantageous for human eye to detect and recognize targets.

    实验结果表明,该算法不仅能使图像融合结果有较大的改善,而且有利于人眼对目标的探测和识别。

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  • The experimental results show that our algorithm is more robust than other methods when non-rigid object has shape deformation and occlusion happens.

    在对金鱼与企鹅序列跟踪的实验结果表明,与现有算法相比,该算法在非刚体目标形状变化以及存在遮挡的情况下,具有更好的跟踪性能。

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  • The experimental results show that our algorithm is more robust than other methods when non-rigid object has shape deformation and occlusion happens.

    在对金鱼与企鹅序列跟踪的实验结果表明,与现有算法相比,该算法在非刚体目标形状变化以及存在遮挡的情况下,具有更好的跟踪性能。

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