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.
Gaussian Naive Bayes (GNB) is a popular supervised learning algorithm to address various classification issues. GNB has strong theoretical basis, however, its performance tends to be hurt by skewed data distribution. In this study, we present an optimal decision threshold-moving strategy for helping...
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....
Gaussian Naive Bayes in Scikit-Learn - Learn how to implement Gaussian Naive Bayes using Scikit-Learn. This tutorial covers the algorithm, implementation, and examples for effective machine learning.
The learning algorithm receives data sequentially for training. 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....
The LR and ANN were used for evaluating the classification performance on hepatocellular carcinoma (HCC) dataset. 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...
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...
Here we introduce Semi-supervised Adaptive Markov Gaussian Embedding Process (SAMGEP), a semi-supervised machine learning algorithm to estimate phenotype event times using EHR data with limited observed labels, which require resource-intensive chart review to obtain. SAMGEP models latent phenotype states...
Supervised classification with condi- tional Gaussian networks: Increasing the structure complexity from naive Bayes. International Journal of Approximate Reasoning, 34(1), 1-25.Aritz, Pedro Larra'naga, and I'naki Inza, 2006, Supervised classification with conditional Gaussian networks: Increasing the ...
the help of software-based pulse filtering regarding area and/or shape of the detector pulses.Here, we present a novel approach for shape-sensitive detector pulse discrimination applying supervised machine learning (ML) based on a naive Bayes classification model using a normally distributed likelihood...