symbolic machine learning

Terms from Artificial Intelligence: humans at the heart of algorithms

The glossary is being gradually proof checked, but currently has many typos and misspellings.

Symbolic machine learning refers to techniques that do not rely on neural networks or other sub-symbolic approaches. Examples include version spaces, k-means and decisoon trees. It is unclear where techniques such as geberic algorithms and swarm computing belong, however a good rule of thumb is to look at the kinds of output rules they produce. If a genetic algorithm has a massive set of paaremeters that are being manipulated, then this is sub-symbolic, but of the outcome is. relatvely simple set of decison rules, then it feels more symbolic.

Used in Chap. 18: page 282