Search Results for "java machine leaning library"

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.

A spring-based 3D graph layout tool written in Java. Rendering is performed in OpenGL via the JOGL library.

The Java Data Mining Package (JDMP) is a library that provides methods for analyzing data with the help of machine learning algorithms (e.g. clustering, classification, graphical models, neural networks, Bayesian networks, text processing, optimization).

LExAu: Learning Expectations Autonomously. Library for on-line data driven statistical machine learning.

3-layer neural network for regression and classification with sigmoid activation function and command line interface similar to LibSVM. Quick Start: "java -jar nen.jar"

A Matlab toolbox for interfacing with the pure JAVA numerical library Snifflib. This toolbox provides convenience m-files for interoperability with Snifflib from within an active Matlab session running a JAVA virtual machine.

Java OpenCL Process Virtual Machine. Spring IoC based framework for complex data analysis with OpenCL computing.

The Wolfram Machine project is an effort to create a set of documentation and useful modules (both hardware and software) for a computing architecture based on the mathematical theories presented in Steven Wolfram's book _A_New_Kind_of_Science_.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

Data Mining Platform is a platform for data mining and analysis. It contains many of the new and sophisticated methods such as kernel-based classification, two-way clustering, bayesian networks, pattern recognition for time series analysis and many other