The results of simulation experiments show effective reduction for large-scale training sample set and improvement of operation efficiency of this algorithm, guaranteeing the classification precision.
仿真实验表明，该算法能在保证不降低分类精度的前提下，对较大规模的样本进行有效的缩减，提高运算效率。
Training sample set inevitably contains gross error in input signal reconstruction of nonlinear multifunctional sensor.
在非线性多功能传感器的信号重构过程中，训练样本集不可避免地夹杂粗差数据。
Training sample set inevitably contains gross error in input signal reconstruction of nonlinear multifunctional sensor.
在非线性多功能传感器的信号重构过程中，训练样本集不可避免地夹杂粗差数据。
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