Search Results for "training"

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation routines. It is written in C++.

The Thot toolkit repository has moved to http://daormar.github.io/thot/ Thot is a toolkit for statistical machine translation. The new Thot toolkit includes fully automatic and interactive machine translation, incremental training of statistical models, parallel estimation, ...

TMVA is a ROOT-integrated toolkit for multivariate classification and regression analysis. TMVA performs the training, testing and performance evaluation of a large variety of multivariate methods. Since 2013, TMVA has been fully integrated with ROOT and is distributed as part of it. The new homepage of TMVA is https://root.cern

This project (CvHMM) is an implementation of discrete Hidden Markov Models (HMM) based on OpenCV. It is simple to understand and simple to use. The Zip file contains one header for the implementation and one main.cpp file for a demonstration of how it works. Hope it becomes useful for your projects.

PyCV is a Python package of modules useful for computer vision tasks. Its current focus is on boosting techniques, Haar-like features, and face detection. PyCV provides the world's fastest method for training a face detector, in a few hours.