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What is FastRandomForest?FastRandomForest is a re-implementation of the Random Forest classifier (RF) for the Weka environment that brings speed and memory use improvements over the original Weka RF. Speed gains depend on many factors, but a 5-10x increase over Weka 3-6-1 on a quad core computer ... [More]
MALLET (A Machine Learning for Language Toolkit) is an integrated collection of Java code useful for statistical natural language processing, document classification, clustering, information extraction, and other machine learning applications to text
This is a Matlab (and Standalone application) port for the excellent machine learning algorithm `Random Forests' - By Leo Breiman et al. from the R-source by Andy Liaw et al. http://cran.r-project.org/web/packages/randomForest/index.html ( Fortran original by Leo Breiman and Adele Cutler, R port ... [More]
FlowClass is a framework that monitors, analyzes and automatically classifies network flows. It is designed to provide differentiated service QoS support to the flows from best-effort applications that do not recognize the QoS. The project consists of the flow classifier, the flow-behavior ... [More]
Description Object oriented C++ library to develop Evolutionary Algorithms based on Probabilistic Models. This library allows the use of predefined state-of-the-art methods, and contains the basic structures to easily create new methods. Dependencies This library uses the OpenCV library. This ... [More]
Nieme is a machine learning library for large-scale classification, regression and ranking. It relies on the framework of energy-based models which unifies several learning algorithms. This framework also unifies batch and stochastic learning which are both seen as energy minimization problems. ... [More]
ML# (ML-Sharp) is a machine learning toolkit created in C# 3.0. It is similar to the Weka library for Java, and it is able to consume some Weka functionality via interop through IKVM. ML# has numerous advantages over Weka including a cleaner API and faster run-time execution.
A text based classification system for documents that focuses on the content rather than tagging. Originally designed to test the viability of pure statistical analysis of documents, but may be extended to use more sophisticated methods.