linear-discriminant-analysis 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
图像处理、推荐系统、矩阵补全、Netflix问题及算法实现中的Python机器学习应用，包括共聚类、Funk SVD、SVD++、非负矩阵分解、Koren邻域模型、Koren集成模型、Dawid-Skene、Platt-Burges、期望最大化、因子分析、ISTA、FISTA、ADMM、高斯混合模型、OPTICS、DBSCAN、随机森林、决策树、支持向量机、独立分量分析、潜在语义索引、主成分分析、奇异值分解、K个最近邻、K个均值、朴素贝叶斯混合模型、高斯判别分析、牛顿法、坐标下降、梯度下降、弹性净回归、脊回归、套索回归、最小二乘、逻辑回归、线性回归
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。
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.
分类器的 MATLAB 工具箱：版本 1.0.7
Multi-sample Unified Discriminant ANalysis
Using Tensorflow and a Support Vector Machine to Create an Image Classifications Engine
This repository contains lecture notes and codes for the course "Computational Methods for Data Science"
implement the machine learning algorithms by python for studying
Using DIgSILENT, a smart-grid case study was designed for data collection, followed by feature extraction using FFT and DWT. Post-extraction, feature selection. CNN-based and extensive machine learning techniques were then applied for fault detection.
In depth machine learning resources
Information Retrieval in High Dimensional Data (class deliverables)
Final Year project based upon Network Intrusion Detection System
Dimension Reduction with LDA
Image Classification with Perceptron and LDA and PCA dimension reduction
This is a binary classification problem related with Autistic Spectrum Disorder (ASD) screening in Adult individual. Given some attributes of a person, my model can predict whether the person would have a possibility to get ASD using different Supervised Learning Techniques and Multi-Layer Perceptron.
This toolbox offers 8 machine learning methods including KNN, SVM, DA, DT, and etc., which are simpler and easy to implement.
Application of Deep Learning and Feature Extraction in Software Defect Prediction
Liveness Tests For Facial Recognition
Automated polysomnography for experimental animal research