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Research tool for interactive training of artificial neural networks.
BNNS is a research tool for interactive training of artificial neural networks based on the Response Function Plots visualization method. It enables users to simulate, visualize and interact in the learning process of a Multi-Layer Perceptron on tasks which have a 2D character. Tasks like the famous two-spirals task or classification of satellite image data.
Featurama is a library that implements various sequence-labeling algorithms. Currently Michael Collins' averaged perceptron algorithm is fully implemented.
BCAR is a library for the associative classification, which denotes "Boosting
Class Association Rules". BCAR provides a general tool for classification tasks
with various types of input data.
openEAR is the Munich Open-Source Emotion and Affect Recognition Toolkit developed at the Technische Universität München (TUM). It provides efficient (audio) feature extraction algorithms implemented in C++, classfiers, and pre-trained models on well-known emotion databases. It is now maintained and supported by audEERING. Updates will follow soon.