self-organizing-map
There are 287 repositories under self-organizing-map topic.
diego-vicente/som-tsp
Solving the Traveling Salesman Problem using Self-Organizing Maps
JustGlowing/minisom
:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps
fcomitani/simpsom
Python library for Self-Organizing Maps
0011001011/vizuka
Explore high-dimensional datasets and how your algo handles specific regions.
ANNetGPGPU/ANNetGPGPU
A GPU (CUDA) based Artificial Neural Network library
felixriese/susi
SuSi: Python package for unsupervised, supervised and semi-supervised self-organizing maps (SOM)
FlorentF9/DESOM
:globe_with_meridians: Deep Embedded Self-Organizing Map: Joint Representation Learning and Self-Organization
giannisnik/som
Pytorch implementation of a Self-Organizing Map
cgorman/tensorflow-som
A multi-gpu implementation of the self-organizing map in TensorFlow
mpatacchiola/pyERA
Python implementation of the Epigenetic Robotic Architecture (ERA). It includes standalone classes for Self-Organizing Maps (SOM) and Hebbian Networks.
CarsonScott/HSOM
Hierarchical self-organizing maps for unsupervised pattern recognition
jonghough/jlearn
Machine Learning Library, written in J
Dotori-HJ/SelfOrganizingMap-SOM
Pytorch implementation of Self-Organizing Map(SOM). Use MNIST dataset as a demo.
enricivi/growing_hierarchical_som
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
farhanchoudhary/Deep-Learning-A-Z-Hands-on-Artificial-Neural-Network
Codes and Templates from the SuperDataScience Course
avinashshenoy97/RusticSOM
Rust library for Self Organising Maps (SOM).
FlorentF9/SOMperf
:globe_with_meridians: SOMperf: Self-organizing maps performance metrics and quality indices
LCSB-BioCore/GigaSOM.jl
Huge-scale, high-performance flow cytometry clustering in Julia
Hatchin/FlowSOM
FlowSOM algorithm in Python, using self-organizing maps and minimum spanning tree for visualization and interpretation of cytometry data
HITS-AIN/PINK
Parallelized rotation and flipping INvariant Kohonen maps
ufvceiec/GEMA
A small Python 3 library to train Self Organizing Maps and use them to classify patterns.
yoch/sparse-som
Efficient Self-Organizing Map for Sparse Data
FlorentF9/sparkml-som
:sparkles: Spark ML implementation of SOM algorithm (Kohonen self-organizing map)
sharmaroshan/Credit-Card-Fraud-Detection
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.
joshspeagle/frankenz
A photometric redshift monstrosity
JRC1995/Self-Organizing-Map
SOM clustering on IRIS dataset
aourednik/text2landscape
Visualize a corpus of texts as a landscape with the aid of text mining, graph visualization and self-organizing maps
iRath96/som-demo
A demo of self-organizing maps using React, TypeScript and three.js
chinazhouzhaoyi/SOM-down-sampling-for-plant-point-clouds
[ISPRS P&RS] Unsupervised shape-aware SOM down-sampling for plant point clouds
dashaub/kohonen4j
Kohonen Self-Organizing Maps in Java
marccasian/KaryML-Framework
Machine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down syndrome, Klinefelter syndrome or Hemophilia. Usually, clinicians are doing chromosome analysis using the karyotype to detect such disorders. The main contribution of the current article consists of introducing a new approach called KaryML Framework, which is extending our previous research: KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps . Our major goal is to provide a new method for an automated karyotyping system using unsupervised techniques. Additionally, we provide computational methods for chromosome feature extraction and to develop an intelligent system designed to aid clinicians during the karyotyping process.
Parth-Vader/ADLAS
Autonomous Dynamic Learning Apprentice System
Rohithram/Self-Organizing-Maps-using-KNN
High Frequency Time series Anomaly Detection using Self Organizing Maps (SOM) which is based on Competitive Learning a variant of the Neural Networks using K Nearest Neighbors
walfonso-uv/NLPCA-SOM
Clustering using Self-Organizing Maps through Non-Linear Principal Components Analysis - Rainfalls in Southwestern Colombia
zyuan-astro/StarGO-OC
Apply a clustering tool based on self-organizing-map to identify open clusters