Learning Vector Quantization (LVQ) is a supervised learning algorithm invented by Teuvo Kohonen, used in the field of machine learning to classify data into predefined sets of classes. LVQ is a type of neural network that focuses on dividing the feature space of data into regions corresponding to different classes.
The LVQ process involves assigning weights to each of the nodes in the network. The weights are adjusted during the training phase so that the model can classify the data more effectively. This adjustment is based on the distance measure just like the k-NN classifier. Thus, through competitive learning, the prototype closest to the training sample is the one that will be updated, moving closer or further away as it favours the classification results.
During the prediction phase, the model uses the network weights to assign a class to the new cases presented to it.
Reference :T. Kohonen. Learning vector quantization for pattern recognition.
The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
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