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++.

...Since then, numerous papers and PhD dissertations have been developed, with applications in the fields of transportation and traffic, logistics, supply chain management, gaming and training, health care, and many others.

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"

NeuroBox is an .NET OOP Library to generate, propagate and train complex neuronal networks with technologies like backpropagation with weight decay, momentum term, manhattan training, flatspot elimination etc.

D-Cog (Declarative-Cognition) is a Java based framework for training software components (reusable, object-oriented, interface-driven components). Instead of programmed, software components are trained by example to get the expected results.

Yawn: Yet Another W* Network: a java framework for developing, training and testing neural networks independently of the model and test environment.

This is a conversion-to-binary training game prototype. Random bits appear, and you must shoot them where they fit onto various bytes (given in octal, hex or even bit ops, decimal or Ascii). Shoot wrongly and you lose that byte, or get it right and win.