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.
Industry 4.0 or the Fourth Industrial Revolution is based on the integration of digital technologies in the production and processing of goods and services.
Read More »Nowadays digital transformation is key in any type of business. The 40% of Spanish companies will not exist in its current form in the next few [...]
Read More »Artificial Intelligence (AI) derives from a series of models or branches that can be used in different areas of people's lives, as well as in different areas of [...]
Read More »Blockchain technology is best known as the computer architecture on which Bitcoin and other cryptocurrencies are based, and it is also known as the [...]
Read More »