⟩ How are neural networks different than Bayesian belief networks along the following dimensions (1) inspect ability of knowledge, (2) need for probabilities acquired from "domain" experts, (3) need for data to train the system, and (4) ability of the system to make classifications based on input data. (Note You may find it helpful to make a 2 × 4 table and include a short phrase or two in each cell.)
Bayesian belief networks are inspectable, known probabilities are required, training data are not needed, and they can classify into multiple categories.
Neural networks are not inspectable, they do not need domain expertise or known probabilities, training data are required, and they are best for a binary classification ("yes" or "no").
