Search Results for "training"

It gives facility of collecting tweets through Twitter Streaming API w.r.t different search criteria and to save tweets in CSV and ARFF (WEKA) file formats.

Gamera is a framework for the creation of structured document analysis applications by domain experts. It combines a programming library with GUI tools for the training and interactive development of recognition systems.

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, ...

...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.

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

The Self-Organizing Map Maker for Education and Research. Features include 3D-visualization of the training process, various 2D and 3D map topologies, easy extensibility to additional topologies.

...It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. Currently, when training corpus, compared with CRF++, CRF# can make full use of multi-core CPUs and only uses very low memory, and memory grow is very smoothly and slowly while amount of training corpus, tags increase. with multi-threads process, CRF# is more suitable for large data and tags training than CRF++ now. For example, in machine with 64GB, CRF# encodes model with more than 4.5 hundred million features quickly.

Software package for building, training and scoring hidden Markov models (HMMs).

NeuroEvolver is a tool for training and experimenting with one particular kind of recurrent neural networks, namely Long Short-Term Memory networks using an evolutionary learning method.

FORce based Cluster Editing (FORCE) is a Java software heuristically solving the graph cluster editing problem on weighted edges using BLAST E-values. It further provides a training mode for heuristic parameter estimation.

FlexCRFs: A Flexible Conditional Random Fields Toolkit for Labeling and Segmenting Sequence Data (this includes a parallel implementation of CRFs called PCRFs to support training CRF models on massively parallel computer systems).

A fast, simple method of establishing musical genre of arbitrary compressed audio files calculated by comparisons with training sets. Program currently consists of a java console program. GUI front end, algorithm upgrades to come.