Learning from Hints

Learning from Hints

Financial Applications of Learning from Hints

The basic paradigm for learning in neural networks is 'learning from examples' where a training set of input-output examples is used to teach the network the target function. Learning from hints is a generalization of learning from examples where additional information about the target function can be incorporated in the same learning process. Such information can come from common sense rules o...

Learning from hints in neural networks

Learning from examples is the process of taking input-output examples of an unknown function f and infering an implementation off. Learning from hints allows for general information about f to be used instead of just input-output examples. We introduce a method for incorporating any invariance hint about f in any descent method for learning from examples. We also show that learning in a neural ...

A Method for Learning From Hints

We address the problem of learning an unknown function by pu tting together several pieces of information (hints) that we know about the function. We introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated for new types of hints. All the hints are represented to the learning process by examples, and exampl...

Hints

The systematic use of hints in the learning-from-examples paradigm is the subject of this review. Hints are the properties of the target function that are known to use independently of the training examples. The use of hints is tantamount to combining rules and data in learning, and is compatible with different learning models, optimization techniques, and regularization techniques. The hints a...