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"

A Simple Extreme Learning Machine implementation in Scala With Breeze

An easy-to-use independent machine learning library for .net. It offers MLP models (including deep RVFL aka ELM) for common ML tasks as well as Reservoir Computer for efficiently solving ML tasks on time series data.

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

Extreme Learning Machines Framework with Python and TensorFlow

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'.

Taking causal inference to the extreme!

Implementação da ELM (Extreme Learning Machine) .

Algorithms proposed in the following paper: OLIVEIRA, Gustavo HFMO et al. Time series forecasting in the presence of concept drift: A pso-based approach. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. p. 239-246.

Extreme Learning Machine Tutorial

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 project it is used a Machine Learning model based on a method called Extreme Learning, with the employment of L2-regularization. In particular, a comparison was carried out between: (A1) which is a variant of incremental extreme learning machine that is QRIELM and (A2) which is a standard momentum descent approach, applied to the ELM.

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.

Python implementation of ELM - with optimized speed on MKL-based platforms; Described in conference paper: Radu Dogaru, Ioana Dogaru, "Optimization of extreme learning machines for big data applications using Python", COMM-2018; Allows quantization of weight parameters in both layers and introduces a new and very effective hidden layer nonlinear…

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.