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如何确定方向LIBLINEAR分离超平面的吗?。How to determine orientation of the separating hyperplane in LIBLINEAR?

显像管的发明是又一次技术革新。The invention of hyperplane kinescopes is another technical innovation.

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该分类面校正了标准的支持向量机的分类误差。The hyperplane revises the error of the standard Support Vector Machines.

最后，通过优化训练，产生一个新的分离超平面。Finally, a new separation hyperplane is generated by the optimization training.

求出了一类扩散过程关于超平面的首中点分布。Distribution of first fitting points on hyperplane is derived in the diffusion process.

用矩阵方法讨论仿射超平面构形的可约性。The reducibility of affine hyperplane arrangements is discussed by using the matrix method.

传统的支持向量机分类超平面对噪声和野值非常敏感。The separating hyperplane of traditional support vector machines is sensitive to noises and outliers.

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针对两类不平衡数据的分离超平面的偏移问题提出一种平衡方法。A balance method for the offset of separation hyperplane of biclassification imbalanced data is proposed.

本文用矩阵方法讨论仿射超平面构形的可约性。In this paper, the reducibility of affine hyperplane arrangements is discussed by using the matrix method.

该类学习机利用线性聚类，提取距分类超平面较近的样本构造改进的学习机。The training data close to the hyperplane are extracted to form the improved learning machines by using linear clustering.

本文主要讨论了超平面构形的模元素及其应用。The main topic of this thesis is about central hyperplane arrangements with modular elements in their intersection lattice.

提出了基于最优超平面与支持向量机思想的最大间隔聚类算法。The paper proposed a new method of clustering with maximal margin based on the idea of optimal hyperplane and support vector machine.

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遗传算法具有全局搜索最优解的特点，是求最优值问题的非常有效的方法。Genetic algorithm is a method to search the optimal solution globally, with which a direct method for searching an optimal hyperplane is given.

SVM的决策函数由邻近分类超平面的部分训练样本-支持向量决定。The decision fuction of SVM is determined by a subset of the training patterns, support vectors, which lies closer to the classification hyperplane.

提出一种基于可判别超平面树的生成模型图像标注方法。In this paper, an image annotation method is proposed which augments the classical generative model with the proposed discriminative hyperplane tree.

此算法的基本思想是在规划问题的可行域中由所建的一个切割面到另一个切割面的不断推进来求取最优的。The idea of this algorithm is to find the optimal solution in the feasible region by an iterative step from one basic standard hyperplane to another.

针对故障诊断等问题中误判造成的损失不同这一特点，定义了基于损失函数的模糊隶属度，并得出了修正后的最优分类面。Aiming at the different loss by error judge in fault diagnosis, the fuzzy membership based on the loss function is defined and optimal hyperplane is modified.

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基于滑模控制技术，确保了该控制器能驱赶系统状态达到事先指定的滑动超平面，从而获得预期的动态性能。Based on the sliding mode control technique, the controller can drive the system into a pre-specified sliding hyperplane to obtain the desired dynamic performance.

引入数据包络分析方法，提出了一种基于贷款可接受案例集的dea型信用评估模型和分类边界为分段线性分离超平面的分类方法。In this paper, we develop a DEA model based on accepted cases set and propose a classification method with piece wise linear separating hyperplane as its boundary.

利用支持向量机把分类特征向量映射到一个高维空间，并在高维空间中构造最优分类超平面以实现信号分类。SVM map inputs vectors nonlinearly into a high dimensional feature space and construct the optimum separating hyperplane in order to realize signal classification.