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supporting hyperplane相关的网络例句

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与 supporting hyperplane 相关的网络例句 [注:此内容来源于网络,仅供参考]

According to the above characteristics of shallow buried target discrimination, the hyperplane SVM, which has achieved the best classification results for many classification problems in different fields by now, has been modified to yield the FHS-SVM, which uses the hypersphere in the kernel space to separate shallow buried target and clutter.

根据浅埋目标鉴别的特点,对在许多领域的分类问题中取得迄今为止最好分类结果的超平面SVM进行了改进,得到利用核特征空间的超球面区分浅埋目标和杂波的FHS-SVM。

At first, four feature attributes were built by content of DNA's four bases. By increasing length attribute of DNA sequence in the space to extent the feature attribute space. Finally, the classification of hyperplane was obtained on the basis of available samples training by using SVC in the feature attribute space.

根据SVM分类器的要求建立特征属性空间,首先由每个DNA中4个碱基的含量得到4个特征属性,然后在此空间中扩充DNA序列长度的属性,最后根据SVM分类器对已知的DNA分类样本做训练得到分类超平面。

A necessary and sufficient condition about the reducibility of central essential hyperplane arrangements is given:a central essential arrangement is irreducible if and only if the matroid is connected.

具体地,本文将对每一个超平面构形,构造一个拟阵,证明中心本质构形不可约的充分必要条件是对应的拟阵是连通的。

The authors present a targets detection method based on the hierarchical clustering which constructs the similarity measuring function according the movement rule of stars and targets,finds the optimal separating hyperplane and classified amount by means of finding the inflexion of error square sum function,then distinguish the targets from stars and noises by two level hierarchical clustering.

提出了基于层次聚类的空间弱小目标检测算法,以星点到参考恒星之间的距离变化为依据,根据恒星和目标的运动特性构造相似性度量函数,通过寻找误差平方和曲线拐点的方法寻找最优分类曲面和分类个数,最后以两层复合分类将恒星、噪声和目标分离。

However, its decision surface is still a hyperplane, In order to enhance its classification ability and solve the limitations of kernel perceptron algorithm, kernel method is used to generalize the α-LMS algorithm without considering whether the projected data set in high dimensional space is linearly separable or not.

另外,经典的核感知器算法也只能解决高维空间中的线性可分问题,对线性不可分问题仍无法收敛,为提高α-LMS算法的分类能力和克服核感知器算法的局限性,本文利用核方法构造了核α-LMS算法。

A new multiclass SVM algorithm based on binary tree was introduced on E-mail filtering. It could convert multiclass problem to binary classification by constructing binary tree. The idea of clustering first and classification later was adopted, and the largest similarity between testing sample and sub-category center and the separation measure of sub-categories were calculated, in oder to construct the optimal class hyperplane of decision-making nodes.

提出一种基于二叉树的多分类SVM算法用于电子邮件的分类与过滤,通过构建二叉树将多分类转化为二值分类,算法采用先聚类再分类的思想,计算测试样本与子类中心的最大相似度和子类间的分离度,以构造决策节点的最优分类超平面。

A new multiclass SVM algorithm based on binary tree was introduced on E-mail filtering. It could convert multiclass problem to binary classification by constructing binary tree. The idea of clustering first and classification later was adopted, and the largest similarity between testing sample and sub-category center and the separation measure of subcategories were calculated, in oder to construct the optimal class hyperplane of decisionmaking nodes.

提出一种基于二叉树的多分类SVM算法用于电子邮件的分类与过滤,通过构建二叉树将多分类转化为二值分类,算法采用先聚类再分类的思想,计算测试样本与子类中心的最大相似度和子类间的分离度,以构造决策节点的最优分类超平面。

While in the transductive support vector machines all the test examples are treated equally, in the fuzzy transductive support vector machines, test examples are treated discriminatively according to their fuzzy labels. Thus, the adjustment of the decision hyperplane is refined, hence a more reliable decision function.

在直推支持向量机中,所有的测试样本被同等对待,而在模糊直推支持向量机中,根据其模糊类别标记的不同,测试样本得到区别对待,从而细化了分类决策面的调整过程,进而可以得到更为可靠的文本分类规则。

In this paper,making use of hyperplane method,we improve the proof of the separation theorem;On the other hand,we use a new method of moving neighborhood to simplify the proof for the continuity of a subconvex function defined on a convex in a normed linear space.

在巴拿赫空间理论中,Hahn-Banach泛函延拓定理作为泛函分析三大基本定理之一,分隔性定理是Hahn-Banach定理的重要应用,本文利用&超平面&的方法,改进了一个分隔性定理的证明;另外,本文利用邻域的&平移&方法,给出了定义在赋范线性空间内的凸集上的次凸泛函连续性的简捷证明。

Keywords: Data distribution, computation distribution, ununiform computational environment, hyperplane.

这是一种单向的数组分布方法,它以简单的方式有效解决了不一致计算环境和不规则数组扫描情况下的数组分布问题。

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