Learning from examples is the process of taking input-output examples of an unknown function
ƒ and infering an implementation of ƒ. Learning from hints allows for general information …

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 us independently of the …

The role of moments in image normalization and invariant pattern recognition is addressed.
The classical idea of the principal axes is analyzed and extended to a more general …

All questions have multiple-choice answers ([a],[b],[c],...). You can collaborate with others,
but do not discuss the selected or excluded choices in the answers. You can consult books …

Moment invariants are evaluated as a feature space for pattern recognition in terms of
discrimination power and noise tolerance. The notion of complex moments is introduced as a …

When feasible, learning is a very attractive alternative to explicit programming. This is particularly
true in areas where the problems do not lend themselves to systematic programming, …

This paper provides a brief introduction to forecasting in financial markets with emphasis on
commodity futures and foreign exchange. We describe the basic approaches to forecasting, …

Learning from hints is a generalization of learning from examples that allows for a variety of
information about the unknown function to be used in the learning process. In this paper, we …

Computer scientists find it increas ingly frustrating to see how ca sually a three-year-old
picks out a tree in a picture. Sophisticated pro grams running on the most powerful …

The maximum drawdown at time T of a random process on [0,T] can be defined informally as
the largest drop from a peak to a trough. In this paper, we investigate the behaviour of this …