| 1. | Ancillary hyperlane method solve sectional intersecting line
辅助超平面法求截交线 |
| 2. | 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 |
| 3. | 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。 |
| 4. | 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。 |
| 5. | 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。 |
| 6. | 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。 |
| 7. | Here the author emphasizes non - linear neural networks used to data mining . the neural networks currently studied are almost linear based on super - flat . usually they need long training time , and are hardly understood
目前的神经网络研究基本上是基于超平面的线性神经网络，通常这种网络存在着学习时间长，网络结构不容易理解等问题。 |
| 8. | 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。 |
| 9. | 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。 |
| 10. | 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 |
