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Project Description
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={},}
Release History

Release History

1.0.4

This version

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1.0.3

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1.0.2

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1.0.1

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1.0.0

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0.6.22

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0.6.21

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0.6.20

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0.6.19

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0.6.18

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0.6.17

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0.6.16

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0.6.15

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0.6.14

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0.6.13

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0.6.12

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0.6.11

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0.6.10

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0.6.9

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0.6.8

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0.6.7

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0.6.6

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0.6.5

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0.6.4

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0.6.3

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0.6.2

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0.6.1

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0.6.0

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0.5.15

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Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
hpelm-1.0.4.tar.gz (36.4 kB) Copy SHA256 Checksum SHA256 Source Mar 23, 2016

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