High Performance toolbox for Extreme Learning Machines.
Extreme learning machines (ELM) are a particular kind of Artificial Neural Networks, which solve classification and regression problems. Their performance is comparable to a classical Multilayer Perceptron trained with Error BackPropagation algorithm, but the training time is up to 6 orders of magnitude smaller. (yes, a million times!)
ELMs are suitable for processing huge datasets and dealing with Big Data, and this toolbox is created as their fastest and most scalable implementation.
Documentation is available here: http://hpelm.readthedocs.org, it uses Numpydocs.
NEW: Parallel HPELM tutorial! See the documentation: http://hpelm.readthedocs.org
 Highlights:

 Efficient matrix math implementation without bottlenecks
 Efficient data storage (HDF5 file format)
 Data size not limited by the available memory
 GPU accelerated computations (if you have one)
 Regularization and model selection (for inmemory models)
 Main classes:

 hpelm.ELM for inmemory computations (dataset fits into RAM)
 hpelm.HPELM for outofmemory computations (dataset on disk in HDF5 format)
 Example usage::

>>> from hpelm import ELM >>> elm = ELM(X.shape[1], T.shape[1]) >>> elm.add_neurons(20, "sigm") >>> elm.add_neurons(10, "rbf_l2") >>> elm.train(X, T, "LOO") >>> Y = elm.predict(X)
If you use the toolbox, cite our open access paper "High Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications" in IEEE Access. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7140733&newsearch=true&queryText=High%20Performance%20Extreme%20Learning%20Machines
@ARTICLE{7140733, author={Akusok, A. and Bj"{o}rk, K.M. and Miche, Y. and Lendasse, A.}, journal={Access, IEEE}, title={HighPerformance Extreme Learning Machines: A Complete Toolbox for Big Data Applications}, year={2015}, volume={3}, pages={10111025}, doi={10.1109/ACCESS.2015.2450498}, ISSN={21693536}, month={},}