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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]
Utilize all kind of Machine Learning approach to enhance antispam techniques. Include antispam addins for mail client, and server-side antispam softwares.
So far we have implemented:Mixture of Gaussians with full covariance Mixture of Gaussians with diagonal covariance matrix (diagonal matrix) Mixture of Gaussians with spherical covaraiance (identity matrix) Two versions of mixture of Gaussians with shrinkage Currently implementing:Mixture of ... [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 tool to find the relevance of candidate terms from a text corpus to any particular topic/sub-domain using a term classification driven approach. The system utilizes the lexical and contextual profiles of the candidate and domain-representing "Resource Terms" (Seed and Ontological). The ... [More]