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ASG ML Comparison Results

Accompanying Research Paper Pending Publication

These are the results from running experiments to compare Symbolic Learning and Statistical Learning. We used Answer Set Grammars and solved these with ILASP for symbolic learning and a Random Forest, General Linear Model and a Fully Connected Feed-Forward Network for statistical learning.

Benchmark Machine

The results were obtained from a machine with the following set of specifications:

Hardware

  • Intel Xeon CPU E7-8870 @ 2.40GHz (80 cores)
  • 576GB RAM DDR3 1333

Operating System

  • Fedora 29 (x86_64)
  • Kernel 5.1.11-200

Software

  • python 3.7.3
  • keras 2.4.2
  • tensorflow 1.14.0 (cpu)
  • h2o 3.24.0.5
  • pandas 0.23.4
  • scikit-learn 0.19.1
  • ILASP version 3.4 -- beta 20/04/2019