What is SOM in neural network?

An SOM is a type of artificial neural network but is trained using competitive learning rather than the error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks.

What does a self Organising map do?

Self-organizing map (SOM) is a neural network-based dimensionality reduction algorithm generally used to represent a high-dimensional dataset as two-dimensional discretized pattern. Reduction in dimensionality is performed while retaining the topology of data present in the original feature space.

What is the purpose of SOM?

the purpose of SOM is that it’s providing a data visualization technique that helps to understand high dimensional data by reducing the dimension of data to map. SOM also represents the clustering concept by grouping similar data together.

What is self-organizing map in AI?

Self Organizing Maps (SOM) or Kohenin’s map is a type of artificial neural network introduced by Teuvo Kohonen in the 1980s. A SOM is an unsupervised learning algorithm trained using dimensionality reduction (typically two-dimensional), discretized representation of input space of the training samples, called a map.

How do I use SOM?

SOM Algorithm

  1. Each node’s weights are initialized.
  2. A vector is chosen at random from the set of training data.
  3. Every node is examined to calculate which one’s weights are most like the input vector.
  4. Then the neighbourhood of the BMU is calculated.
  5. The winning weight is rewarded with becoming more like the sample vector.

How does SOM work in general?

Summary

  1. A self-organizing map (SOM) is a grid of neurons which adapt to the topological shape of a dataset, allowing us to visualize large datasets and identify potential clusters.
  2. An SOM learns the shape of a dataset by repeatedly moving its neurons closer to the data points.

How is self-organizing map implemented?

The Algorithm Behind Training Self-Organizing Maps

  1. Initialize all grid weights of the SOM.
  2. Repeat until convergence or maximum epochs are reached. Shuffle the training examples. For each training instance x. Find the best matching unit BMU. Update the weight vector of BMU and its neighboring cells.

What is map in neural network?

mAP probably stands for mean Average Precision. The AP provides a measure of quality across all recall levels for single class classification, it can be seen as the area under the precision-recall curve. Then the mAP is the mean of APs in multi-class classification.