The result proves that predicting network time-delay by SVM has greater accuracy than by linear prediction algorithm.
结果证明，和线性预测算法相比，采用支持向量机预测网络延时具有较高的正确率。
The principle of finding optimized decision boundary give SVM excellent performance on linear separatable problems.
最优分类超平面原理使SVM在解决线性可分问题时有很好的表现。
The support vector machine (SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.
支持向量机(SVM)是一种线性机器，广泛用于模式分类和非线性回归。
To detect objects quickly, a new method is presented to construct a cascade of linear classifiers with L-SVM (Lagrangian Support Vector Machine, L-SVM).
为了实现目标的快速检测，提出了一种新的基于拉格朗日支持向量机(L -SVM)的线性级联式分类器的构造方法。
Even for binary, linear classification it is data dependent whether it is better to train the geometrical model (SVM?) or a probabilistic one.
即使是二进制的，线性分类它是依赖于数据是否是更好的列车的几何模型(SVM ？)或概率。
The simulation shows that the SVM model is highly reliable, it offers very important applicable values to implement realtime online control and online prediction for linear reciprocating generator.
仿真表明，SVM模型可靠高效，对实现直线振动发电机的实时在线控制及在线预测具有非常重要的应用价值。
The new type of linear reciprocating generator is studied by using the support vector machine (SVM) modeling method that features precisely realtime performance.
采用具有准确实时性的支持向量机(SVM)建模方法对新型直线振动发电机进行研究。
Moreover, SVM can convert a nonlinear learning problem into a linear learning problem in order to reduce the algorithm complexity by using the kernel function concept.
又由于采用了核函数思想，使它将非线性问题转化为线性问题来解决，降低了算法的复杂度。
Considering the learning and extrapolating ability as well as the parameter optimizing time, linear kernel is determined to be used in SVM in the analysis of diesel engine exhaust emissions.
综合考虑SVM的学习能力、外推能力及寻优时间，决定选择线性核函数作为SVM在柴油机尾气分析中的核模型。
The model of the nonlinear system is obtained by LS-SVM, the offline model is linearize at each sampling instant and uses linear predictive function control methods to obtain the control law.
该算法采用LS-SVM回归建立非线性系统的预测模型，然后，将离线模型在每个采样周期关于当前采样点进行线性化，同时利用线性预测函数控制方法求解解析的控制律。
In accordance with the features of non-linear and time varying for ferment process, a support vector machines (SVM) model is established for estimating the concentration of product.
针对非线性时变的发酵过程，建立了用于产物浓度预估的支持向量机(SVM)模型。
For non-linear problem, the forecasting technique of pre-classification and later regression was proposed, based on the classification approach of Support Vector Machine (SVM).
针对非线性问题，提出了基于支持向量机分类基础的先分类、再回归的预测方法。
So one should prefer non-linear models like SVM with kernel or tree based classifiers that bake in higher-order interaction features.
因此，每个人都应该选择适合高阶交互特征的带核SVM或基于树的分类器。
The system performance has been greatly improved when the Log-linear and Rank-SVM models in machine learning are applied to fuse a few systems to get the last results list.
为了提高系统性能，应用机器学习中的Log -linear和Rank - SVM模型提出了基于系统融合的结果链表二次调序方法。
The system performance has been greatly improved when the Log-linear and Rank-SVM models in machine learning are applied to fuse a few systems to get the last results list.
为了提高系统性能，应用机器学习中的Log -linear和Rank - SVM模型提出了基于系统融合的结果链表二次调序方法。
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