| 1. | 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 |
| 2. | 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。 |
| 3. | 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。 |
| 4. | 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。 |
| 5. | 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。 |
| 6. | 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。 |
| 7. | 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 |
| 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 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。 |
