The nonlinear support vector machine multiuser detector can approach the optimum multiuser detector in a better way than the other linear detectors.
另外，支撑矢量机多用户检测器的非线性特性可以比线性检测器更好地逼近最优检测器。
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。
The lab result shows the high accuracy, 78.33%, came from the combination of right source-yuan points and linear core of support vector machine.
实验结果显示，使用右十二经脉原穴配合支援向量机的线性核心是所有实验中准确率最高者，达到78.33%。
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。
Support vector machine is a kind of machine study algorithm based on statistic theory, it has special advantage in solving small sample, non-linear and high dimension mode recognition.
支持向量机是一种基于统计理论的机器学习算法，在解决小样本、非线性及高维模式识别中有独特的优势。
Our tests show that the veracity and stability of support vector machine are both obviously superior to the traditional linear classifying methods.
多组试验表明，支持向量机方法的准确性和稳定性较传统线性分类方法均具有明显优势。
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。
Chaos and support vector machine theory has opened up a new route to study complicated and changeable non-linear hydrology time series.
混沌和支持向量机理论为研究复杂多变的非线性水文时间序列开辟了新的途径。
Experimental results indicate that the support vector machine performs a number of unique advantages in solving the small sample size, non-linear problems.
经过实验证明支持向量机在解决小样本、非线性问题中表现出很好的优势。
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。
The support vector machine possesses the ability to solve problems such as small sample, non-linear, and the directed acyclic graphs algorithm has the advantage of high classification accuracy.
支持向量机具有较好的解决小样本、非线性问题的能力，而DAG算法具有分类精度高的优点。
The support vector machine possesses the ability to solve problems such as small sample, non-linear, and the directed acyclic graphs algorithm has the advantage of high classification accuracy.
支持向量机具有较好的解决小样本、非线性问题的能力，而DAG算法具有分类精度高的优点。
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