The contribution work is to apply machine learning algorithm for emotion classification, it gives less time consumption without interfere human labeling. The Gaussian Naive Bayes classifier works on testing dataset with help of huge amount of training dataset. Measure the performance of POMS & Gaussian...
前面我们学习的都是 discriminant learning algorthm, 直接对 p(y|x)p(y|x) 进行建模,或直接学习 X→YX→Y 的映射。GDA 和 naive bayes 是 generative learning algorithm, 对 p(x|y)p(x|y) 建模,再通过贝叶斯公式计算 p(y|x)=p(x|y)p(y)p(x)p(y|x)=p(x|y)p(y)p(x) Gaussian ...
Gaussian Naive Bayes Classifier This is an FPGA accelerated solution of Gaussian NaiveBayes classification algorithm. It provides up to 100x speedup compared to a single threaded execution on an Intel Xeon CPU.SpecificationsClassesFeatures up to 64 up to 2048...
Simple Gaussian Naive Bayes classifier implementation. It also implements 5-fold cross-validation. Compared performance with Zero-R algorithm. Dataset Glass.csv Attribute and Class Information: RI: refractive index Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4...
Gaussian Discriminant Analysis (GDA) is a statistical algorithm used in machine learning for classification tasks. It is a generative model that models the distribution of each class using a Gaussian distribution, and it is also known as the Gaussian Naive Bayes classifier....
The EM algorithm 上面看到使用EM来拟合混合高斯问题,但这只是EM的一个特例 这章会推导出EM的一般形式,他可以解决各种含有隐变量的预估问题(estimation problems with latent variables.) Jensen's inequality 先介绍一下Jensen不等式 首先通过下面的图理解一下,当f是凸函数的时候 ...
In real-time, the user can manipulate the parameters of the target distribution, and see the learning algorithm react. An implementation of Online Gaussian Naive Bayes in included, with a forward-weighted option to facilitate adaptation. Other algorithms can be plugged in by conforming to a ...
The EM algorithm 上面看到使用EM来拟合混合高斯问题,但这只是EM的一个特例 这章会推导出EM的一般形式,他可以解决各种含有隐变量的预估问题(estimation problems with latent variables.) Jensen's inequality 先介绍一下Jensen不等式 首先通过下面的图理解一下,当f是凸函数的时候 ...
Pereira [9] used K-means clustering algorithm with some different methods for oversampling: random method, SMOTE method, Borderline SMOTE method, and G-SMOTE method. The KNN, LR, DT algorithms were used for evaluating the classification performance on 13 datasets. Gradient Boosting Classifier (GBC...
This repository is based upon thecourse materialby Stanford University. Professor Andrew Ng may not teach the most comprehensive lectures but he has inspired millions to study data science. This repository attempts to replicate every algorithm mentioned in the course as well as the popular ones out...