均值收敛

词组短语

convergence in mean

与均值收敛相关的网络例句 [注：此内容来源于网络，仅供参考]

The experimental results prove that the convergence speed of the proposed method is non-linear and the use of convergence points can lower the complicacy of process of iteration.

实验结果表明，改进后的高斯核均值漂移算法以超线性的速度收敛，收敛点的应用降低了收敛过程中的计算量。

In this paper, we propose an algorithm for finding global minimization by discrete mean value-level set, the convergence of this algorithm is proved, a termination rule and some numerical examples are given.

西文提出了一个只需计算函数值的离散均值—水平集求全局最优的方法，给出了算法的终止准则，证明了算法的收敛性，并给出了某些数值例子。

In the study of the asymptotic properties, the testing statistics converges to a zero mean Gaussian process and is very sensitive to the to the local alternatives converging to the null one at a parameter convergent rate.

研究发现，文中的检验统计量在原假设下渐近收敛于一个零均值的高斯过程，并且对以参数速度收敛到原假设的被择分布特别敏锐。

An adaptive over-relaxed fast dynamic mean shift was proposed to speed up the convergence of Gaussian mean shift.

为了解决高斯核均值漂移算法收敛速度慢、计算效率不高的问题，提出自适应over-relaxed快速动态更新方法改进高斯核均值漂移算法。

N adaptive over-relaxed fast dynamic mean shift was proposed to speed up the convergence of Gaussian mean shift.

bstract 为了解决高斯核均值漂移算法收敛速度慢、计算效率不高的问题，提出自适应over-relaxed快速动态更新方法改进高斯核均值漂移算法。

The convergence of the mean shift procedure to the closest mode of the underlying distribution is proven, both for the Epanechnikov kernel and the general case of kernels with convex and monotonic decreasing profile. The smooth trajectory property of the mean shift is also demonstrated.

均值平移算法对于Epanechnikov 核函数的收敛性在本文中得到了证明，进而推出均值平移算法收敛的充要条件是核函数具有凸的、单调递减性质的轮廓；揭示了均值平移过程运动轨迹的平滑性。

In this paper, the theory, algorithm, and experiment of automatic object detection and tracking are studied in depth. It is firstly pointed out that the essential of Mean Shift method is a special Newton-Gaussian method. A new method named Fast Mean Shift is established to stretch the conservative step of Mean Shift method. The convergence and validity of this new method are proved in theory. And it is also proved that the convergence speed of Fast Mean Shift is faster than that of Mean Shift. The contrast experiments of searching the maximum possibility density of random of data sets in plane and 3D space are done. The results show that this new method can reduce the iterations greatly. A new object tracking method based on Fast Mean Shift is built to improve the object tracking performance, which is shown in the face tracking experiment with the tennis sequence provided by the Ohio State University, and the car tracking experiment with the car sequence provided by Kalsruhe University. The face trcking experiment with highly noised images show that the object tracking method based on Fast Mean Shift has strong anti-jamming ability. A new fast color object detection technology based on characteristic color is established, which use characteristic color distribution to compute the characteristic color vector of any area in an image quickly. With the high performance search method, the fast object detection is achieved. At last, using object tracker based on Fast Mean Shift and color object detector based on characteristic color with the Kalman filter, PID controller, searial communication and other technologies, automatic object detection and tracking system with control system is built. The availability and anti-jamming ability of this system are verified by some object detection and tracking tests in different scenes.

本文对目标自动识别与跟踪进行了理论、计算、试验三方面的深入研究，主要包括：首次指出了目标跟踪技术中常用的均值迁移方法的本质为一种特殊的高斯-牛顿方法，改进了均值迁移方法步长取值保守的弱点，建立了快速均值迁移方法，证明了该方法的收敛性、有效性以及收敛速度优于均值迁移方法；进行了平面和3维随机分布数据集的最大概率密度搜索对比试验，试验结果表明，快速均值迁移方法大大减少了迭代次数；建立了基于快速均值迁移的目标跟踪方法，利用俄亥俄州立大学提供的乒乓球序列图像和卡斯鲁厄大学的汽车序列图像，对人脸和汽车目标跟踪性能分别进行了对比试验，并进行了高噪声人脸图像目标跟踪试验，结果表明，基于快速均值迁移的目标跟踪方法有效提高了目标跟踪性能，具有很强的抗干扰能力；建立了一种新型彩色目标自动识别方法，采用特征色彩分布函数实现了对任意图像区域特征色彩矢量的快速计算，建立了高效的搜索方法，实现了彩色目标的快速识别；将基于快速均值迁移方法的目标跟踪方法、基于特征色彩的目标识别方法与卡尔曼滤波、PID控制、串行通讯等技术结合，建立了带有控制系统的快速目标自动识别与跟踪系统，并在不同场景下进行了目标自动识别与跟踪试验，验证了快速目标自动识别与跟踪系统的有效性和抗干扰能力。

With a view of this, we propose the key theorem and discuss the bounds on the rate of uniform convergence...

基于此种考虑，本文提出了样本受零均值噪声影响下的学习理论的关键定理，并讨论了零均值噪声样本下的学习过程一致收敛速度的界。

With a view of this, we propose the key theorem and discuss the bounds on the rate of uniform convergence of learning processes based on ERM principle when samples are corrupted by zero-expect noise.

基于此种考虑，本文提出了样本受零均值噪声影响下的学习理论的关键定理，并讨论了零均值噪声样本下的学习过程一致收敛速度的界。

The method solves the limitation of converging to the local infinitesimal point in medical image segmentation,and adopts the initial algorithm to assure the initial searching scope of genetic algorithm which is better accommodable than standard genetic algorithm with fuzzy C-means clustering,speeding up the convergence of genetic algorithm.

该算法除了解决模糊C均值聚类算法在医学图像分割中容易陷入局部最优解的问题，而且采用的初值化算法比标准的遗传模糊C均值聚类算法能确定更合适的遗传算法的初始搜索范围，从而加速了遗传算法的收敛过程。

与均值收敛相关的网络解释 [注：此内容来源于网络，仅供参考]

convergence in mean：均值收敛

convergence half | 半会聚角 | convergence in mean | 均值收敛 | convergence in probability | 概率性收敛