Gaussian process latent variable model (GPLVM) is a popular manifold method recently proposed for dimensional reduction.
高斯过程隐变量模型是最近提出的比较流行的无监督降维方法。
During the process, noise model is built by Gaussian statistics algorithm, which can extract moving object contour accurately and quickly.
在此过程中，使用高斯统计的方法建立噪声模型，使得该方法能更准确快速提取目标的轮廓。
And then, taking into account the specific process of automatic image annotation, we built the automatic image annotation model based on Gaussian mixture model.
再针对具体的自动标注过程，建立了基于高斯混合模型的自动图像标注模型。
Based on Gaussian random process model and continuous-time system in time domain, this paper analyzes the effect on baseband and intermediate frequency sampling due to clock jitter.
该文从时域连续信号角度出发，按照高斯随机过程模型，分析了时钟抖动对基带和中频线性调频信号信噪比的影响并给出了近似公式。
Based on Gaussian random process model and continuous-time system in time domain, this paper analyzes the effect on baseband and intermediate frequency sampling due to clock jitter.
该文从时域连续信号角度出发，按照高斯随机过程模型，分析了时钟抖动对基带和中频线性调频信号信噪比的影响并给出了近似公式。
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