Many organizations today have more than very large databases; they have databases that grow without limit at a rate of several million records per day. Mining these continuous dat...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Recent work has shown the promise in using local-search “probes” as a basis for directing a backtracking-based refinement search. In this approach, the decision about the next...
Alexander Nareyek, Stephen F. Smith, Christian M. ...
Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classification accuracy, training time, and (in the ca...
Dynamic evaluation is a technique for producing multiple results according to a decision tree which evolves with program execution. Sometimes it is desired to produce results for ...