From entry symbolic machine learning in glossary Artificial Intelligence: humans at the heart of algorithms
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 decision trees. It is less clear where techniques such as genetic 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 paremeters that are being manipulated, then this is sub-symbolic, but of the outcome is. relatively simple set of decision rules, then it feels more symbolic.
Used in Chap. 3: page 45