Solving the Traveling Salesman Problem using Self-Organizing Maps
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Updated
Dec 24, 2023 - Python
Solving the Traveling Salesman Problem using Self-Organizing Maps
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
Codes and Templates from the SuperDataScience Course
Python library for Self-Organizing Maps
A multi-gpu implementation of the self-organizing map in TensorFlow
🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
Pytorch implementation of a Self-Organizing Map
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
A GPU (CUDA) based Artificial Neural Network library
Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.
Machine Learning Library, written in J
Parallelized rotation and flipping INvariant Kohonen maps
Pytorch implementation of Self-Organizing Map(SOM). Use MNIST dataset as a demo.
Self-Organizing Map [https://en.wikipedia.org/wiki/Self-organizing_map] is a popular method to perform cluster analysis. SOM shows two main limitations: fixed map size constraints how the data is being mapped and hierarchical relationships are not easily recognizable. Thus Growing Hierarchical SOM has been designed to overcome this issues
🌐 SOMperf: Self-organizing maps performance metrics and quality indices
Hierarchical self-organizing maps for unsupervised pattern recognition
Huge-scale, high-performance flow cytometry clustering in Julia
It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters.
FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data
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