KOHONEN Meaning and
Definition
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Kohonen refers to a type of artificial neural network algorithm named after its creator, Teuvo Kohonen. Also known as self-organizing maps (SOM), Kohonen networks are unsupervised learning systems that aim to simulate human brain activity. The algorithm is commonly utilized in the field of machine learning for clustering and visualizing complex data patterns.
The primary objective of a Kohonen network is to organize and represent high-dimensional input data in a two-dimensional or low-dimensional grid. The network consists of a set of nodes known as neurons, which are arranged in a lattice-like structure. Each neuron possesses a weight vector that determines its influence on the data inputs. During the learning process, these weights are adjusted through a competitive mechanism, where the neuron with the closest weight vector to the input data is selected as the winner.
By iteratively updating the weights of the neurons, the Kohonen algorithm enables the neural network to gradually classify the input data into different clusters. The resulting mapping on the grid allows for visualizing similarities and relationships between different patterns within the data.
Kohonen networks find applications in various fields, such as data mining, image recognition, and exploratory data analysis. They offer a powerful tool for organizing, analyzing, and understanding complex datasets, facilitating the extraction of meaningful information from large amounts of data.
