Is it even logically possible to have a type of machine learning that doesn't fall into those three paradigms?
Supervised: a dataset of inputs and outputs are fed to an algorithm which learns a function to generate outputs from inputs
Unsupervised: a dataset of only inputs are fed into an algorithm and the algorithm produces label outputs that can be used to make predictions from new data
Reinforcement: a dataset of only inputs are fed to an algorithm, which eventually learns to generate outputs that maximize a reward function
Isn't semisupervised ultimately just supervised with some output data missing? Is "punishment" learning viable? Would Hebbian learning count as something different? Most existing artificial neural net structures don't operate with the "fire together wire together" logic.