Semantics of object representation in machine learning
Justification of learning tools requires investigations on data representations. Machine learning act exhibits multiple levels of abstraction. Idealization is realized with feature selection and model construction. Different idealization schemes are considered with distinct representational goals. Latent variables are problematic in their semantics.
object representation abstraction idealization latent variables philosophy of machine learning