ID	原文	译文
1373	因此超奈奎斯特技术成为了一种具有高频谱利用率的新型传输方法，也为带宽受限的通信场景带来了新的解决方案，并且其在卫星信道以及第五代移动通信的回程链路中的应用成为了研究热点。	Therefore, FTN signaling hasbecome a new bandwidth-efficient modulation method which offers a promising solution to the communication scenario withscarce bandwidth. Furthermore, faster-than-Nyquist signaling has also become a hot topic in the research of satellite commu-nication and backhauling technique in 5G communication standard.
1374	本文对超奈奎斯特传输的概念与原理、容量计算、实现方式、检测方法以及性能与应用做了详细的总结并对其目前的技术难点进行了讨论。	This paper provides a summary of FTN signaling in termsof its concept and theory, capacity, implementation, detection, as well as performance and applications, respectively. Some discussions of current research difficulties are also given in the paper.
1375	本文提出了一种新的带有同步化特征选择的聚类算法，称为“具有同步化特征选择的迭代紧凑非平行支持向量聚类算法”(IT-NHSVC-SFS)。	In this paper, a new clustering algorithm with simultaneous feature selection is proposed, which is called it-erative tighter nonparallel support vector clustering with simultaneous feature selection(IT-NHSVC-SFS).
1376	在具有两个非平行超平面的学习模型中使用迭代(交替)优化算法完成聚类，同时引入两种类型的正则项，分别是欧几里得范数和无穷范数，	In learning withtwo nonparallel hyperplanes model, we use the iterative (alternating)optimization algorithm to achieve clustering, and at thesame time introduce two types of regularizes, the Euclidean norm and the infinite norm, respectively.
1377	欧几里得范数用于提升聚类模型的泛化能力，无穷范数实际上是对两个非平行超平面进行同步化地隐式特征抽取，从而降低来自于不相关特征的聚类噪音，保证了模型的聚类精度，	Euclidean norm cluste-ring model is used to improve the generalization ability and the infinite norm actually fulfills implicit feature extraction forthe two nonparallel hyperplanes in order to reduce data noises from irrelevant features, and the clustering precision of themodel is guaranteed.
1378	并引入一组束缚变量(bounding variables)避免无穷范数的最大化操作，将非凸优化问题转化成二次凸优化问题。	We also introduce a set of bounding variables to avoid maximization operation of the infinite norm, converting the non-convex optimization problem into a quadratic convex optimization problem.
1379	同时，由于新提出的模型体现着“最大间隔”的思想，因此具有良好的泛化能力。	Meanwhile, because the newmodel embodies the idea of " maximum margin" , it has good generalization ability.
1380	为了方便实现两个非平行超平面同步化的特征选择过程，文中将非平行超平面 SVM(Nonparallel Hyperplane SVM，NHSVM)作为 IT-NHSVC-SFS 算法的基础模型，因此和 TWSVM 以及它的变体模型不同的是:只需要求解一个二次规划问题(QP 问题)就可以同时得到两个最优超平面。	IT-NHSVC-SFS chooses nonparallel hy-perplanes SVM (NHSVM)as the basis of the algorithm model. Unlike TWSVM and its variant models, only a quadratic programming problem(QP problem)needs to be solved to get the two optimal hyperplane simultaneously.
1381	同时，新算法在原有的 NHSVM 模型的约束条件集合中新添加了两组等式约束条件，从而无需进行原有模型中的两个大矩阵的求逆操作，降低了计算复杂度。	The new algorithm adds two sets of equality constraints in the constraint set of the original NHSVM model, which can avoid the inverse operation of two largematrices and reduce the computational complexity.
1382	此外，在 IT-NHSVC-SFS 模型中，用拉普拉斯损失函数(Laplacian loss measure)代替了 NHSVM 模型原有的铰链损失函数(hinge loss function)，避免了算法早熟收敛(prema-ture convergence)。	In addition, in the IT-NHSVC-SFS model, the Laplacian loss function re-places the original hinge loss function in NHSVM to avoid premature convergence.