14 skills found
lmjohns3 / KohonenKohonen vector quantizers (SOM, NG, GNG)
spiglerg / Kohonen SOM TensorflowTensorflow implementation of the Kohonen Self Organizing Map
EklavyaFCB / EMNIST Kohonen SOMUsing Kohonen's algorithm to make a Self Organising Map using the EMNIST database on handwritten alphabets.
FlorisHoogenboom / Som Anomaly DetectorImplementation of Kohonen SOM for anomaly detection purposes.
mljs / Somself-organizing map (SOM) / Kohonen network
KaroRonty / SelfOrganizingStockMarketAnalysis of the US stock market using Kohonen's SOM algorithm
FlorentF9 / Sparkml Som:sparkles: Spark ML implementation of SOM algorithm (Kohonen self-organizing map)
albertnadal / KohonenNeuralNetworkC implementation of the Kohonen Neural Network (SOM algorithm)
jcfaracco / Xpysom DaskXPySom-dask is a minimalistic implementation of batch Self Organizing Map algorithm using Dask. This is a mirror of the original XPySom.
HRakesh / Healthcare Data Analysis On PIMA Indian Diabetes DatabaseKnowledge Discovery in Database. * In this project we focused our analysis on applying data analysis techniques, create visualizations and interpret the models using histograms, scatter plots and many other visual plots etc. to uncover the reason for high diabetic outcome among the PIMA Indian women. * Predict whether the patient is diagnosed with diabetes based on diagnostic measurements available in the dataset. * We obtained this dataset from Kaggle and built some supervised explanatory models (classification tree and logistic regression) and predictive models (KNN); also tried Unsupervised techniques such as K-means Clustering and Kohonen's Self-Organizing Map (SOM).
nikkonrom / Neural CompressingPre-alpha version of compressing images algorithm based on neural networks (Kohonen SOM)
leobispo / SomSOM - Self organizing Map is a Swing application that implements the Self organizing map algorithm. Self-organizing map (SOM) is a type of artificial neural network that is trained using unsupervised learning to produce low-dimensional representation of the training samples while preserving the topological properties of the input space. Self-Organizing Map showing US Congress voting patterns visualized in Synapse Self-Organizing Map showing US Congress voting patterns visualized in Synapse This makes SOM useful for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling. The model was first described as an artificial neural network by the Finnish professor Teuvo Kohonen, and is sometimes called a Kohonen map. Like most artificial neural networks, SOMs operate in two modes: training and mapping. Training builds the map using input examples. It is a competitive process, also called vector quantization. Mapping automatically classifies a new input vector.
KosalaHerath / Kohonen SomMATLAB implementation of a Kohonen Self Organizing Map (SOM).
johnlime / UnitNeuronsC++ neuron-based neural network library
