IEEE

Data-Driven Open-Set Fault Classification of Residual Data Using Bayesian Filtering

Abstract: Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on ...
IEEE

Naive Bayes classification algorithm based on small sample set

Abstract: Naive Bayes algorithm is one of the most effective methods in the field of text classification, but only in the large training sample set can it get a more accurate result. The requirement ...
GitHub

Bayesian machine learning notebooks

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.
eLife

Barcode-free multiplex plasmid sequencing using Bayesian analysis and nanopore sequencing

This study provides an important computational tool for analyzing and deconvoluting a pool of plasmids sequenced without barcoding using nanopore long-read sequencing. The tool, which has been ...
GitHub

BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus

Implementation of BANSAC, a new guided sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously ...
Visual Studio Magazine

Multinomial Naive Bayes Classification Using the scikit Library

A full-code demo from Dr. James McCaffrey of Microsoft Research shows how to predict the type of a college course by analyzing grade counts for each type of course. General naive Bayes classification ...
Scientific Research Publishing

A Novel Efficient and Effective Preprocessing Algorithm for Text Classification ()

1 College of Information Science and Technology, Jinan University, Guangzhou, China. 2 University of Birmingham Joint Institute, Jinan University, Guangzhou, China. Text classification is an essential ...
PNAS

Wide and deep neural networks achieve consistency for classification

While neural networks used in practice are often very deep, the benefit of depth is not well understood. Interestingly, it is known that increasing depth is often harmful for regression tasks. In this ...
Visual Studio Magazine

Naive Bayes Classification Using C#

Notice that all the data values are categorical (non-numeric). This is a key characteristic of the naive Bayes classification technique presented in this article . If you have numeric data, such as a ...
Frontiers

Beating Naive Bayes at Taxonomic Classification of 16S rRNA Gene Sequences

Naive Bayes classifiers (NBC) have dominated the field of taxonomic classification of amplicon sequences for over a decade. Apart from having runtime requirements that allow them to be trained and ...
Frontiers

An Explainable Bayesian Decision Tree Algorithm

Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically ...