Search Results for "bayes networks"

...Data analysis and visualization receive dedicated coverage via NumPy, pandas, matplotlib, seaborn, and pyecharts, followed by an applied machine learning track with kNN, trees, Bayes, regression, clustering, ensembles, and neural networks.

...The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution, Bayesian networks can be dropped into a mixture just as easily as a normal distribution, and hidden Markov models can be dropped into Bayes classifiers to make a classifier over sequences. Together, these two design choices enable a flexibility not seen in any other probabilistic modeling package.

UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports Bayesian networks, influence diagrams, MSBN, OOBN, HBN, MEBN/PR-OWL, PRM, structure, parameter and incremental learning. Please, visit our wiki (https://sourceforge.net/p/unbbayes/wiki/Home/) for more information. Check out the license section (https://sourceforge.net/p/unbbayes/wiki/License/) for our licensing policy.

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

...Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests;  neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. While some mathematical foundation is provided, it is not essential for understanding the implementations. ...

Projeny (Probablistic Networks Generator in Java) is a graphical (Java SWT) front-end to BNT (Bayes Net Toolbox for Matlab). Projeny requires BNT, JMatLink and a Matlab back-end. There is no installable release package, but source code is available on SVN - please check out from SVN to use Projeny. Projeny was started with BNJ as the base.

software for identifying modules in networks (e.g. "community detection"), as described in "a bayesian approach to network modularity" (physical review letters 100, 258701 (2008); http://link.aps.org/abstract/PRL/v100/e258701).

Matlab Classification Toolbox contains implementations of the following classifiers: Naive Bayes, Gaussian, Gaussian Mixture Model, Decision Tree and Neural Networks. This toolbox allows users to compare classifiers across various data sets.

A Visual Studio .NET C++ application can perform machine learning using genetic algorithm, naive bayes, KNN, and Artificial Neural Networks (ANNs) read and processed from any standard ARFF.