Extreme Learning Machine implemented in Pytorch
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Updated
Apr 6, 2018 - Python
Extreme Learning Machine implemented in Pytorch
Unsupervised Extreme Learning Machine(ELM) is a non-iterative algorithm used for feature extraction. This method is applied on the IRIS Dataset for non-linear feature extraction and clustering using k-means, Self Organizing Maps(Kohonen Network) and EM Algorithm
A Numpy Implementation of Extreme Learning Machine (ELM)
A tensorflow implementation of OS-ELM (Online Sequential Extreme Learning Machine)
Notes from course of Computational Intelligence at Federal University of Ceará 2019.1
Implementation of Supervised Extreme Learning Machine for binary classification
Feature selection using genetic algorithm for ELM and MLP
Pytorch implementation of Extreme Learning Machine
A Python Implementation of Kernel Extreme Learning Machine for Ordinal Regression
(Code) A new workload prediction model using extreme learning machine and enhanced tug of war optimization
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.
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…
localization through STI-WELM fingerprinting
Multi-Objective Optimization of ELM for RUL Prediction
Authorship Attribution in Social Media & Chat Biometrics & Behavioral Biometrics
This is my project using Extreme Learning Machine (ELM) based on Guang-Bin Huang Paper
UAV Flight Analysis and ML-powered Rolling Launch Control System. Written in Python and q/kdb+. Deployed at:
Machine Learning for Synthetic Aperture Radar Autofocus
As described in "Towards Full On-Tiny-Device Learning: Guided Search for a Randomly Initialized Neural Network"
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