Machine Learning

Learning Vector Quantization for Machine Learning

Last Updated on August 15, 2020

A downside of K-Nearest Neighbors is that you need to hang on to your entire training dataset.

The Learning Vector Quantization algorithm (or LVQ for short) is an artificial neural network algorithm that lets you choose how many training instances to hang onto and learns exactly what those instances should look like.

In this post you will discover the Learning Vector Quantization algorithm. After reading this post you will know:

  • The representation used by the LVQ algorithm that you actually save to a file.
  • The procedure that you can use to make predictions with a learned LVQ model.
  • How to learn an LVQ model from training data.
  • The data preparation to use to get the best performance from the LVQ algorithm.
  • Where to look for more information on LVQ.

This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems.

If you have any questions on LVQ, leave a comment and I will do my best to answer.

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