gaussian-mixture-models 开源项目推荐（610 个）

ddbourgin /numpy-ml

机器学习，在numpy

neka-nat /probreg

Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

ldeecke /gmm-torch

Gaussian mixture models in PyTorch.

conradsnicta /armadillo-code

Armadillo: fast C++ library for linear algebra (matrix maths) & scientific computing

jayshah19949596 /Machine-Learning-Models

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

omerbsezer /Generative_Models_Tutorial_with_Demo

Generative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..

0xzkzhao /Wind-Speed-Analysis

An elegant probability model for the joint distribution of wind speed and direction.

gionanide /Speech_Signal_Processing_and_Classification

前端语音处理旨在从语音话语的短期片段中提取适当的特征，这些片段称为框架。它是解决任何使用语音或音频（如音乐）模式识别问题的前提步骤。这里，我们关注声音障碍的分类。也就是说，开发两类分类器，能够区分例如声带麻痹患者和健康受试者的话语。人类语音产生系统的数学建模表明，全极系统功能是合理的[1-3]。因此，线性预测系数（LPC）成为建模语音短期频谱幅度的首选。LPC导出的中空系数保证区分系统（如声道）贡献与激发贡献。考虑到人耳特性，mel频率的中频频分相系数（MFCC）成为语音频谱包络的描述特征。与MFCCC类似，感知线性预测系数（PLP）也可以推导。上述说话传统特征将与由交集神经网络（CNN）（例如自编码器）提取的不可知性特征进行测试[4]。模式识别步骤将基于高斯混合模型分类器、K最近邻分类器、贝叶斯分类器以及深度神经网络。马萨诸塞州眼耳医院数据集（MEEI-Dataset）[5]将被利用。在应用层面，将开发一个用于Python特征提取和分类的库。可信的公开资源将被用于实现我们的目标，比如KALDI。将与[6-8]进行比较。

SuperKogito /Voice-based-gender-recognition

使用梅尔频率倒谱系数（MFCC）和高斯混合模型（GMM）的基于语音的性别识别

AlexanderFabisch /gmr

Gaussian Mixture Regression

wentaoyuan /deepgmr

PyTorch implementation of DeepGMR: Learning Latent Gaussian Mixture Models for Registration (ECCV 2020 spotlight)

ransaka /GMM-from-scratch

The only guide you need to learn everything about GMM

sandipanpaul21 /Clustering-in-Python

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

Mayurji /MLWithPytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

borchero /pycave

Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.

jonasrothfuss /fishervector

Improved Fisher Vector Implementation- extracts Fisher Vector features from your data

jobovy /extreme-deconvolution

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

mr-easy /GMM-EM-Python

Python implementation of EM algorithm for GMM. And visualization for 2D case.

applied-geodesy /jag3d

Java·Applied·Geodesy·3D - Least-Squares Adjustment Software for Geodetic Sciences

Xiaoyang-Rebecca /PatternRecognition_Matlab

特征降维投影和分类器模型通过训练数据集学习，并应用于对测试数据集进行分类。本文比较了几种特征降维方法：主成分分析（PCA）、线性判别分析（LDA）及其核方法（KPCA、KLDA）。相应地，还比较了几种分类……