介绍了基于支持向量机的概率密度估计。
A new method for density estimation was developed based on the Support Vector Machine (SVM).
红外目标的描述利用目标区域的灰度分布，该灰度分布通过核概率密度估计建立。
Infrared objects are represented by the intensity distribution, which is defined by the kernel-based density estimation.
多元统计过程介绍了三种主要的方法：主元分析法、偏最小二乘法和核函数概率密度估计法。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
通过相隔固定的帧差值阅值化得到背景样本值，并采用高斯核密度估计方法计算背景灰度的概率密度函数。
The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.
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