Computational Learning Theory and Natural Learning Systems - v. 1 : Constraints and Prospects

These contributions converge on an intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning.

Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in th context of experiments with actual questions such as, What is a natural system?

How should learning systems gain from prior knowledge?

If prior knowledge is important, how can we quantify how important?

What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar?

Is there a fundamental difference in the kine of task a neural network can easily solve as opposed to those a symbolic algorith can easily solve?