The pixel level frame/field difference statistic is used by film mode detection based on gaussian classifier.
该技术利用逐像素的帧、场变化统计以及基于高斯分类的电影模式状态识别对电影模式源进行检测和处理。
This feature vector made the Gaussian Mixture Model classifier outperform MFCC and Differential MFCC features in classification.
该混合特征使得高斯混合模型分类器可获得比使用MFCC特征及其差分MFCC更好的分类性能。
Neural network; Classifier; Gaussian function; Clustering; Fuzzy decision; Back propagation
神经网络；分类器；高斯函数；聚类；模糊决策；反向传播
An online mixture of experts is used to divide the HRRP data into several regions within which online Gaussian process classifier make predictions.
通过一种在线混合专家将HRRP数据在线地分割成若干个近似平稳的区域，在各个区域内使用平稳协方差函数的在线高斯过程分类器。
And the model parameters are used in a ML-based Gaussian Mixture Model classifier with 26 dimensions features including 12 order Mel-Frequency Cepstral Coefficient、8 order delta-cepstral、4 order delta-delta-cepstral、1 order delta-energy and 1 order delta-delta-energy parameters.
而所得的模型参数被用於具有26维度其中包含12维的梅尔倒频谱参数、8维的delta cepstral参数、4维的delta-delta-cepstral参数、1维的delta-energy参数和1维的 delta-delta-energy参数置入高斯混合模型辨识器(Gaussian Mixture Model，GMM)。
Using of SPOT5 satellite images and Aerial images, we have analyzed the land use changing and environment changing of the source domain by Gaussian Maximum Likelihood Classifier and category changing detection, and accomplished the data of Vegetation, terrain, topography, land use and environment changing in the source domain successfully.
该文主要利用高分辨率SPOT5卫星影像，辅以航空影像数据，采用高斯最大似然方法和类别变化检测法，进行水源区域土地利用变迁和环境变迁分析，成功取得了水源区域内植被、地形、地貌、土地利用及变迁情况资料。
Reinforcement Learning for Continuous Spaces Based on Gaussian Process Classifier
基于高斯过程分类器的连续空间强化学习
This paper principally discusses the training problem of Gaussian basis function classifier which can be used for classification. For basis function classifier, how to correctly initialize the number of network hidden nodes and their parameters is very important.
本文主要研究高斯基函数分类器的训练问题，对基函数分类器来说，如何确定网络的初始隐层节点数和隐层节点参数是一个重要问题。
In the model training and recognition module, author use the Gaussian mixture model as a classifier, which in comparison with other classifiers, can better identify abnormal audio difference.
在模型训练与识别模块中，使用了高斯混合模型作为分类器，它与其他分类器相比，能更好的分辨出异常声音的差别。
The Comparison between SVM with Gaussian Kernel and Radial Basis Function Classifier
具有高斯核函数的支撑矢量机与径向基函数分类器的比较
We adopt the output of Gabor filters as feature and Gaussian mixture model (GMM) as the classifier.
本文采用Gabor滤波器的输出结果作为特征，高斯混合模型（GMM）作为分类器对织物瑕疵分类。
Proposed is a hybrid neural network classifier with high performance and simple structure that makes a substitution for the highly complex Gaussian mixture model (GMM). This classifier is composed of a self-organizing map neural network (SOFMNN) and a probabilistic neural network (PNN).
针对这样的问题，提出一种高性能、结构简单的基于自组织映射（SOFMNN）和概率神经网络（PNN）的混合神经网络分类器以取代目前常用的高斯混合模型（GMM）分类器。
Then the study utilizes the Linear Discriminant Classifier, Quadratic Discriminant Classifier, Support Vector Machine, SVM (containing Gaussian kernel) as well as the k-Nearest Neighbor Classifier in the experiment.
再分别利用线性判别分类器、二次判别分类器、支持向量机（含高斯内核）以及k值-最近的邻位序列分析分类器进行实验。
In this approach the eigenvectors of power signals to be discriminated, which are analyzed and processed by adaptive Gaussian representation (AGR), are input into neural network classifier.
将经过自适应高斯基表示（AdaptiveGaussianRepresentation，AGR）分析处理的电力信号特征向量输入神经网络分类器进行识别。
Because cyclic higher-order statistics can depress Gaussian noise, cyclic higher-order cumulant can be used to structure invariants of classifier.
由于高阶循环统计量具有抗平稳噪声的能力，从而在高阶循环累积量域构成了分类特征不变量。
It is proven that the solution of one-class SVM using the Gaussian kernel can be normalized as an estimate of probability density, and can be used to obtain the Bayesian classifier.
证明采用高斯核的一类SVM，其解可以归一化为密度函数，并把该密度函数看作类条件概率密度的平滑估计，构造贝叶斯分类器。
A classifier based on EMGD_HMM (Ergodic Mixed Gaussian Density HMM) is proposed to classify speech, music, and their mixed audio.
提出了一种基于各态历经混合高斯密度隐马尔可夫模型（EMGDHMM）的音频分类器，用于语音、音乐以及它们混合声音的分类。
Incremental nonlinear proximal support vector classifier for multi - class classification based on Gaussian ker-nel is deduced.
在以往工作的基础上对近轴支持向量机进行了研究，推导了基于高斯核函数的非线性近轴支持向量机的增量式算法。
In feature-level case, fully polarization information from SAR is used to combine with spectral characteristic from SPOT images, mainly to enhance land cover classification as well. Speckle noise was removed by Lee filter, followed by the maximum likelihood classifier based on different distribution was used for SAR and SPOT images (Based on Wishart distribution and multivariate Gaussian distribution respectively), to extract the conditional probability of each pixel for each class.
首先使用李式滤波器滤除全偏极雷达资料杂讯，接下来同样是使用採用最大似然法来分类融合影像，(不同的在於全偏极雷达影像使用Wishart机率分布，在光学影像採用multivariateGaussian机率分布)將每个类别中每个像元属於某个类别的机率值计算出来，再利用Dempster-Shafer evidence theory来结合这些类别的机率值。