Two Challenges of Correct Validation in Pattern Recognition

Supervised pattern recognition is the process of mapping patterns to class labels that define their meaning. The core methods for pattern recognition have been developed by machine learning experts but due to their broad success an increasing number of non-experts are now employing and refining them. In this perspective I will discuss the challenge of correct validation of supervised pattern recognition systems, in particular when employed by non-experts. To illustrate the problem I will give three examples of common errors that I have encountered in the last year. Much of this challenge can be addressed by strict procedure in validation but there are remaining problems of correctly interpreting comparative work on exemplary data sets, which I will elucidate on the example of the well-used MNIST data set of handwritten digits.