Aiming at the problem of large optimization size in dynamic outlier detection, this paper proposes a Kernel-based Real-time Outlier Detection (KROD) method.
针对动态野点数据检测过程中可能存在的优化规模过大的问题，提出了一种基于核方法的实时野点检测方法：KROD。
This paper based on ambiguity function theory analyzes the cause of cross-components and gives the optimization algorithm of adaptive TFD with radially-gaussian kernel.
文中以模糊函数理论为基础，分析了时频分析产生交叉项的原因，给出了以径向高斯函数为核函数的自适应时频分析优化算法。
In the paper, GBGM-GA is seen the optimization technique combining KPCA and GA, and is suitable to the optimization selection of kernel function parameter.
本文采用高斯变异遗传算法作优化技术，实现了KPCA和GA的集成，适合核函数参数的优化选择。
Finally, it realizes with the same percolation, searches the optimization calculate way inside the DBMS kernel, and handles the spatial and non-spatial data.
实现以相同的过滤算法、查询优化算法在DBMS内核处理空间与非空间数据。
This thesis deals with gray image. The kernel of segmentation is pixel clustering. It belongs to optimization problem.
本文处理的对象是灰度图像，分割的核心是对像素进行聚类，属于优化问题。
Thus, it makes low-power optimization a kernel faced in RFID transponder design.
这样，低功耗优化就成为无源射频识别标签设计中的一个核心问题。
To solve resources optimization deployment problem for complicated parts in networked manufacturing, an approach taking the process flow of part as kernel was proposed.
为实现复杂零件网络化制造的资源优化配置，提出一种以零件工艺流程为核心的制造资源优化配置方法。
Moreover, a weight optimization scheme for the multi-kernel was proposed by maximizing the Margin Maximization Criterion(MMC)based on the method of Lagrange multipliers.
进而，使用拉格朗日乘子法优化最大边缘准则（MMC），提出了多重核权值优化算法。
Mean shift based image segmentation algorithm is a kind of kernel density estimation based feature space analysis algorithm, and the nature of it is statistical optimization.
均值漂移算法是一种基于核密度梯度估计的特征空间分析算法，其实质是一种统计优化过程。
However, the large computing cost and uncertain kernel method and standard uncertainty of parameter optimization don't contribute to the development of SVM.
然而，计算代价过大、核函数方法和参数寻优标准不确定性限制了SVM的应用范围。
However, the large computing cost and uncertain kernel method and standard uncertainty of parameter optimization don't contribute to the development of SVM.
然而，计算代价过大、核函数方法和参数寻优标准不确定性限制了SVM的应用范围。
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