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.
通过极大化该边界获得最优投影向量，同时避免因类内离散度矩阵奇异导致的小样本问题。
Through maximalizing the margin, we can obtain the optimal projection vector, and avoid the small sample size problem due to singularity of the within-class scatter.
为此采用全样本对称周期延拓的方法进行边界延拓。
Therefore, full-sampled symmetric periodic extension method is adopted here in order to alleviate boundary effect.
文中提出了一种基于边界近邻的最小二乘支持向量机，采用寻找边界近邻的方法对训练样本进行修剪，以减少了支持向量的数目。
A new least squares support vector machines based on boundary nearest was proposed, which reduced the number of support vector by using boundary nearest methods pruning the training Sam.
同时，给出了不同条件下密集颗粒样本的生成方法，分析了边界应力、初始构型、加载速率、粒子间的摩擦系数等对颗粒系统目标构型的影响。
The influences of boundary stress, initial configuration, loading rate and friction coefficient between particles on the state and configuration of dense granular system are also examined.
通过对正常和攻击样本的聚类分析，定义聚类簇中心的边界面接近度因子，实现对标准SVM二次式的改进；
Based on clustering normal and attack training samples, by defining approaching degree of boundary surface of every clustering center, quadratic expression of standard SVM is improved;
该方法首先基于邻域灰度极值提取边界候选图像，然后以边界候选象素及其邻域象素的二值模式作为样本集，输入边缘检测神经网络进行训练。
The method uses a logical judgment algorithm to get edge candidate images, and then edge pixels and their neighbor pixels compose the binary samples of the BP neural network.
该方法首先基于邻域灰度极值提取边界候选图像，然后以边界候选象素及其邻域象素的二值模式作为样本集，输入边缘检测神经网络进行训练。
The method uses a logical judgment algorithm to get edge candidate images, and then edge pixels and their neighbor pixels compose the binary samples of the BP neural network.
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