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 Back-Propagation 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 HP-ELM 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 in-memory models)

Main classes:

• hpelm.ELM for in-memory computations (dataset fits into RAM)
• hpelm.HPELM for out-of-memory 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={High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications}, year={2015}, volume={3}, pages={1011-1025}, doi={10.1109/ACCESS.2015.2450498}, ISSN={2169-3536}, month={},}