In its MACHINE LEARNING section, XYOM offers the possibility to use a multilayer perceptron (Copyright (c) 2015-2016 Ulf Biallas) applied to the recognition of taxonomic groups with the use of metric properties (size, shape).
The general idea is to train the multilayer perceptron (MLP) with known (or reference) data, i.e., metric properties of known taxonomic groups, then to use the output of the trained MLP, called “weights“, to recognise unknown individuals.
Fake unknown data
Reference data file, and its corresponding subdivision.
The reference data are randomly subdivided into training and testing sets, the process of learning is iterative, and stopped when the testing set is correctly identified at a level of 75%.
Then, the fake unknown (or true unknown) are tentatively identified using the weights.
In the next XYOM version, the user will be allowed to enter also the subdivision corresponding to the fake unknown.
Copyright (c) 2015-2016 Ulf Biallas
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