Search Results for "extreme learning machine"

* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: https://arma.sourceforge.net/download.html * Documentation: https://arma.sourceforge.net/docs.html * Bug reports: https://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/armadillo-lib/armadillo-code

Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

* Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive Apache 2.0 license, useful for both open-source and proprietary (closed-source) software * Can be used for machine learning, pattern recognition, computer vision, signal processing, bioinformatics, statistics, finance, etc * Downloads: http://coot.sourceforge.io/download.html * Documentation: http://coot.sourceforge.io/docs.html * Bug reports: http://coot.sourceforge.io/faq.html * Git repo: https://gitlab.com/conradsnicta/bandicoot-code

ECOC PAK is a C++ Library for the Error Correcting Output Codes classification framework. It supports several coding and decoding strategies as well as several classifiers.

Now part of Apache's Mahout machine learning project at http://mahout.apache.org/-- please see there for latest info and code and releases and support!

Machine learning toolkit for unsupervised and semi-supervised clustering that demonstrates excellent results on real-world data (see Bekkerman et al. ICML-2005 and ECML-2006).

A genetic algorithm framework to allow the evolution of synapse weights and topologies of artificial neural networks.