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Extra resources for Artificial Intelligence Applications and Innovations: 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) 2006, June 7–9, 2006, Athens, Greece
Although it requires much more computation, as it involves two learning phases (one for Sammon's mapping, one for the neural network), it should perform at least as well as SAMANN . In Mao and Jain's implementation the network is able to project new patterns after training - a property Sammon's mapping does not have. A drawback of using SAMANN is that the original dataset has to be scaled for the artificial neural network to be able to find a correct mapping, since the neural network can only map to points in the sigmoid's output interval, (0,1).
Semerato, A comparative analysis of methods for pruning decision trees, IEEE Trans. Pattern Anal. Mack Intell, 19(5), 476-492 (1997) 7. L. P. Cordelia, P. Foggia, C. Sansone, F. Tortorella, M. Vento, Reliability Parameters to Improve Combination Strategies in Multi-Expert Systems, Pattern Analysis and Application, 2, 205-214 (1999) 8. S. Paul, S. Kumar, Subsethood- Product Fuzzy Neural Inference System (SuPFuNIS), IEEE Trans. Neural Networks, 13(3), 578-599 (2002) Retraining the Neural Network for Data Visualization Viktor Medvedev, Gintautas Dzemyda Institute of Mathematics and Informatics Akademijos str.
A plan for the navigation problem is a path through the world from the initial state (start state) to the goal state (fmal state). Usually, the initial state include the complete map of the world. During navigation some unexpected situations may arise, and the robot cannot follow the plan, so it needs to replan starting from the current position as the new start position. In real-time systems it is necessary to tradeoff the quality of a plan against the computational cost of planning, and it is necessary to interleave planning and execution, to combine reactive with deliberative capabilities.