The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, inclu...

We consider boundedly rational learning processes in which players have a priori limited set of behavior rules. A behavior rule is a function from information to a stage-game action, which reflects the available information and one’s reasoning about how others act. Commonly used behavior rules include the adaptive rule and the conservative rule (inertia). Sophisticated players may use iterative...

Evaluating the performance of a data mining technique is a fundamental aspect of machine learning. Evaluation method is the yardstick to examine the efficiency and performance of any model. The evaluation is important for understanding the quality of the model or technique, for refining parameters in the iterative process of learning and for selecting the most acceptable model or technique from...

We consider the training of neural networks in cases where the nonlinear relationship of interest gradually changes over time. One possibility to deal with this problem is by regularization where a variation penalty is added to the usual mean squared error criterion. To learn the regularized network weights we suggest the Iterative Extended Kalman Filter (IEKF) as a learning rule, which may be ...