Silas, see Naive Bayes classifier for how an “observable characteristics graph” similar to Network 2 should work in theory. It’s not clear whether Hopfield or Hebbian learning can implement this, though.
To put it simply, Network 2 makes the strong assumption that the only influence on features such as color or shape is whether the object is a a rube or a blegg. This is an extremely strong assumption which is often inaccurate; despite this, naive Bayes classifiers work extremely well in practice.
Silas, see Naive Bayes classifier for how an “observable characteristics graph” similar to Network 2 should work in theory. It’s not clear whether Hopfield or Hebbian learning can implement this, though.
To put it simply, Network 2 makes the strong assumption that the only influence on features such as color or shape is whether the object is a a rube or a blegg. This is an extremely strong assumption which is often inaccurate; despite this, naive Bayes classifiers work extremely well in practice.