通过引进人工数据(Artificial data) , 大大简化了 算法的复杂度, 提高了算法的效率。得到了项目参 数的估计值及能力的后验分布后, 不用迭代即可以 求得能力参数的贝...
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Experiments using artificial data and actual business data testify the validity of this method. It can improve the traditional K-means effect well.
采用人工数据和实际商业数据的实验证明该方法能有效地改善传统的聚类效果。
The experiments on the artificial data and real-world data show that the improved algorithm is robust and effective, and the application to the performance evaluation is successful.
人工数据与真实数据上的实验表明,改进的算法健壮且有效,在绩效考核上应用成功。
Simulation results for artificial data show that the proposed method gives good performance on reducing the effects of noise and improves the regression accuracy and generalization.
仿真实验结果显示了该方法有效地减少了噪声的影响,改进了回归的精度,增强了推广能力。
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