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
NeuPy is a Tensorflow based python library for prototyping and building neural networks
Log Anomaly Detection - Machine learning to detect abnormal events logs
Implementation of SOM and GSOM
Self organizing Kohonen map in Python with periodic boundary conditions
SOMns: A Newspeak for Concurrency Research
🌐 Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.
Recursive Self-Organizing Map/Neural Gas.
Pytorch implementation of Self-Organizing Map(SOM). Use MNIST dataset as a demo.
Parallelized rotation and flipping INvariant Kohonen maps
🌐 SOMperf: Self-organizing maps performance metrics and quality indices
Huge-scale, high-performance flow cytometry clustering in Julia
Using Self-Organizing Maps for Travelling Salesman Problem
FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data
Self Organizing Map (SOM) is a type of Artificial Neural Network (ANN) that is trained using an unsupervised, competitive learning to produce a low dimensional, discretized representation (feature map) of higher dimensional data.
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