因此，当一位科学家有兴趣研究生活在海平面下某一温度层边缘的微生物，这台水下自动探测机器人能够找到温度梯度的边界，并在那里找到最佳样本。
So if a scientist wanted to study the microorganisms living on each side of a temperature gradient, the AUV would find the boundary, follow it, and pick the best spot to take samples.
最后以另一个大的比例减去位于距异类中心较远的对分类不起作用的样本点，以便提取具有代表性的边界向量。
Finally, the other large proportion is decided to reduce those sample points lie on the further from the different class center so that the representative boundary vectors can be extracted.
基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。
The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.
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