frobt.2014.00005.pdf (5.03 MB)
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 nonexperts. 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.
Funding
Green brain; G0924; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/J019690/1
History
Publication status
- Published
File Version
- Published version
Journal
Frontiers in Robotics and AIISSN
2296-9144Publisher
FrontiersExternal DOI
Issue
5Volume
1Page range
1-6Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2014-10-01First Open Access (FOA) Date
2014-10-01First Compliant Deposit (FCD) Date
2014-10-01Usage metrics
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