Artificial neural networks have proven to be a successful, general method for inductive learning from examples. However, they have not often been viewed in terms of constructive ...
As machine learning (ML) systems emerge in end-user applications, learning algorithms and classifiers will need to be robust to an increasingly unpredictable operating environment...
We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
Classifying the endgame positions in Chess can be challenging for humans and is known to be a difficult task in machine learning. An evolutionary algorithm would seem to be the ide...
Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervi...