Final Project for Bacherol in Industrial Engineering to UPV, Valencia, Spain.

As described in "Towards Full On-Tiny-Device Learning: Guided Search for a Randomly Initialized Neural Network"

Implementation of Learning Vector Quantization (LVQ) and Extreme Learning Machine (ELM) with Iris Dataset

Extreme Learning Machines Framework with Python and TensorFlow

Extreme Learning Machines on MNIST (try)

Exercises and assignments made during a Computational Intelligence class at Federal University Of Ceará.

This repository contains a Machine Learning project, implemented in python, named 'Crop Recommendation System using Extreme Learning Machine'.

Implementação da ELM (Extreme Learning Machine) .

Implementation of paper: Rádli, R., & Czúni, L. (2023). Deep Randomized Networks for Fast Learning

Notes from course of Computational Intelligence at Federal University of Ceará 2019.1

A Neural Network from scratch (Extreme Learning Machine), trained on MNIST (97% accuracy).

Extreme Learning Machine for image classification implemented using Cuda C++ and cuBLAS

In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense.

Implementação da Rede Neural Função base radial (FBR, ou RBF do inglês Radial base function) proposta no Projeto prático 6.5 do livro "Redes Neurais Artificiais para engenharia e ciências aplicadas" do autor Ivan Nunes da Silva.

Eigen3实现的极限学习机算法，不含其他任何依赖；Extreme learning machine algorithm implemented by Eigen3 library, and no any other dependencies

Extreme Learning Machine and Meta-heuristic algorithms implemented in C using threads to solve a Feature Selection problem related to the generation of Seawave Energy

An unofficial python implementation of the discriminative graph regularized Extreme Learning Machine (GELM) proposed by Yong Peng et al., with sklearn compatibility

Implemented ELM and H-ELM algorithms and applied H-ELM on MNIST dataset for digit recognition

localization through STI-WELM fingerprinting