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.
In order to identify the customer's needs, it is necessary to know their opinion, as this helps to detect where you should improve, what acceptance you [...]
Read More »How is artificial intelligence helping us? Artificial intelligence (AI) has gone from being the stuff of science fiction movies to a [...]
Read More »Achieving business goals and tracking success is an important aspect of improving any business. In sales, measuring the progress of [...]
Read More »What is Digital Transformation? The industrial revolution profoundly changed the society of the 19th century, but the digital transformation of the [...]
Read More »
