hyperplane造句
例句与造句
- Research on distance from point in to hyperplane in euclidean space
欧氏空间中点到超平面的距离研究 - The separating hyperplane of traditional support vector machines is sensitive to noises and outliers
摘要传统的支持向量机分类超平面对噪声和野值非常敏感。 - When traditional support vector machines separate data containing noises , the obtained hyperplane is not an optimal one
使用传统的支持向量机对含有噪声的数据分类时，所得到的超平面往往不是最优超平面。 - Svm maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in the spade to realize modulation recognition
支撑矢量机把各个识别特征映射到一个高维空间，并在高维空间中构造最优识别超平面分类数据，实现通信信号的调制识别。 - The multiple - hyperplane classifier , which is investigated from the complexity of optimization problem and the generalization performance , is the explicit extension of the optimal separating hyperplanes classifier
多超平面分类器从优化问题的复杂度和运行泛化能力两方面进行研究，是最优分离超平面分类器一种显而易见的扩展。 - It's difficult to find hyperplane in a sentence. 用hyperplane造句挺难的
- Chapter 2 has systematically discussed machine learning problem , which is the basic of svm , with statistical learning theory or slt . secondly , chapter 3 has educed the optimal hyperplane from pattern recognition
第二章探讨了支持向量机理论基础? ?学习问题，尤其是对vapnik等人的统计学习理论（ slt ）结合学习问题作了系统的阐述。 - Pcc takes the normal vector of a hyperplane as the projecting direction , onto which the algebraic sum of all samples " projections is maximized , such that samples in one class can be separated well from the other by this hyperplane
主分量分类器是在两类样本投影代数和最大的前提下，获得最佳投影方向(分类面法方向) ，实现样本分类。它的不足之处在于： 1 - For this problem , a separating hyperplane is designed with the principle of maximizing the distance between two class centers , and a novel support vector machine , called maximal class - center margin support vector machine ( mccm - svm ) is designed
为了解决这个问题，本文以两个类中心距离最大为准则建立分类超平面，构造一个新的支持向量机，称作类中心最大间隔支持向量机。 - Is that if a set of points in n - space is cut by a hyperplane , then the application of the perceptron training algorithm will eventually result in a weight distribution that defines a tlu whose hyperplane makes the wanted cut
）下的结论是，如果n维空间的点集被超平面切割，那么感知器的培训算法的应用将会最终导致权系数的分配，从而定义了一个tlu ，它的超平面会进行需要的分割。 - In addition , all the system states are on the sliding hyperplane at the initial instant , the reaching phase of smc is eliminated and the global robustness and stability of the closed - loop system can be guaranteed with the proposed control strategy
此外，控制策略使得系统的初始状态已经处于滑模面上，从而消除了滑模控制的到达阶段，进而确保了闭环系统的全局鲁棒性和稳定性。 - In chapter 4 we obtain the helly number for hyperplane transversal to translates of a convex cube in r ~ ( d ) . where we prove that the helly number for such families is 5 when d = 2 , and is greater than or equal to d + 3 when d 3
在第4章中我们探讨了o中超平面横截单位立方体平移形成的集族的heily数，证得碑中此heily数为5 ，在呼中此heily数z民并推广至呼，在胸中此heily数d 3 - If these points can be cut by a hyperplane - in other words , an n - dimensional geometric figure corresponding to the line in the above example - then there is a set of weights and a threshold that define a tlu whose classifications match this cut
如果这些点可以被超平面换句话说，对应于上面示例中的线的n维的几何外形切割，那么就有一组权系数和一个阈值来定义其分类刚好与这个切割相匹配的tlu 。 - The idea is proposed that those increased date , which near the separating hyperplane , is significant for the forming of the new hyperplane , whenever these date are classed by the former hyperplane to test error set berr or test right set bok
与传统的增量学习方法不同，本文中，作者认为那些在分类面边缘增加的数据对分类面的改变都起着重要的作用，无论这些数据被初硕士论文支持向量机在图像处理应用中若干问题研究始分类器p划分到测试错误集berr或者测试正确集b 。 - By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built , svm presents a lot of advantages for resolving the small samples , nonlinear and high dimensional pattern recognition , as well as other machine - learning problems such as function fitting
Svm的基本思想是通过非线性变换将输入空间变换到一个高维空间，然后在这个新的空间中求取最优分类超平面。它在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，并能够推广应用到函数拟合等其他机器学习问题中。 - The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability . to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem . after optimization all vectors are evaluated a weight . the vector whose weight is not zero is called support vector
而寻找最优分类超平面需要解决二次规划这样一个特殊的优化问题，通过优化，每个向量(样本)被赋予一个权值，权值不为0的向量称为支持向量，分类超平面是由支持向量构造的。
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