Neuronal network

Neuronal network (NN), Machine Learning (ML), Artificial intelligence (AI)

NN is one of the AI techniques that can be used for instance to predict groups membership.

It is necessary to previously train the NN with known data. With XYOM, this machine learning (ML) step is accessible in the “MACHINE LEARNING” section.

Provided a “weights” file has been produced after the ML step, the group membership attribution is accessible (also through the “IDENTIFICATION” section).

In sum:

1/ “MACHINE LEARNING”

You must enter 3 arguments

I. The (fake) unknown file (could be true unknown also, for identification)

II. The reference data file

III. The subdivision of the reference data

2/ “IDENTIFICATION” (cannot be performed without the previous ML step)

You must call 2 files

I. The unknown data file

II. The “Weights” file as produced by the ML step.

3/ I M P O R T A N T    R E M A R K S    for   LANDMARKS DATA 

The general instruction lines above do not directly apply when data are landmark-based shape variable, because shape variables derived from landmarks are sample dependent. Please see hereunder what steps you must follow to tentatively assign landmarks configurations to other such landmarks configurations.

MACHINE LEARNING of XYOM for LANDMARK-BASED SHAPE variables:

  • Do not enter RAW COORDINATES of LANDMARKS or of PSEUDOLANDMARKS as inputs
  • Unknown and reference matrices must have the same number of columns
  • Landmarks must be in the same order for unknown and reference specimens.
  • For landmark-based shape data, the identification of unknown should use exclusively the MACHINE LEARNING section of XYOM (it should NOT use the IDENTIFICATION section).
  • Please consider the following steps (These steps are  NOT automatically implemented by XYOM):
    • 1. Concatenate raw landmarks of unknown and reference individuals
    • 2. Perform a GPA (Generalised Procrustes Analysis) on these concatenated data (single file)
    • 3. Split the file of Procrustes Residuals (ending by …_ORP.txt) into unknown and reference data (2 files)
    • 4. Use these 2 files of Procrustes Residuals as input for MACHINE LEARNING process generating WEIGHTS, and see the report
    • 5. Do not keep these weights as valid weights for new identification analyses

 

MACHINE LEARNING of XYOM for PSEUDOLANDMARK-BASED SHAPE data:

For pseudolandmark-based shape data (NEF), the process is easier because they are not sample dependent. The identification of unknown may use the MACHINE LEARNING section AND the IDENTIFICATION section.

Please consider the following steps for pseudolandmarks data (These steps are  NOT automatically implemented by XYOM):

  • 1. Compute the NEF from unknown data
  • 2. Compute the NEF from reference data
  • 3. Select the same number of columns for each file (unknown and reference).
  • 4. MACHINE LEARNING section: compute and save the WEIGHTS
  • 5. New unknown (NEF, same number of columns as learning input) could be tentatively identified using the IDENTIFICATION section of XYOM

 

See here ->  the detailed MLP (multilayer perceptron ) configuration by XYOM (Please remember that to adjust the parameters of the multilayer perceptron in terms of number of hidden layers, number of neurons by layer, etc. you should test FAKE unknown to check for overfitting).

LICENCE

The Machine learning procedures of XYOM makes use of javascript libraries under MIT licence.

Copyright (c) 2015-2016 Ulf Biallas

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