Download Artificial Intelligence Applications and Innovations: 3rd by Ilias Maglogiannis, Kostas Karpouzis PDF

By Ilias Maglogiannis, Kostas Karpouzis

Artificial Intelligence functions construct on a wealthy and confirmed theoretical heritage to supply options to quite a lot of actual lifestyles difficulties. The ever increasing abundance of data and computing strength permits researchers and clients to take on higly attention-grabbing matters for the 1st time, comparable to functions delivering custom-made entry and interactivity to multimodal details in keeping with personal tastes and semantic ideas or human-machine interface platforms using details at the affective kingdom of the consumer. the aim of the third IFIP convention on synthetic Intelligence purposes and techniques (AIAI) is to compile researchers, engineers, and practitioners drawn to the technical advances and enterprise and commercial functions of clever structures. AIAI 2006 is concentrated on supplying insights on how AI may be applied in actual global functions.


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Extra info for Artificial Intelligence Applications and Innovations: 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), 2006, June 7-9, ... in Information and Communication Technology)

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Let d.. denote the distance between X. and X. in the input space, and d^. denote the distance between the corresponding points Y. andy. in the projected space. The Euclidean distance is fi-equently used. The projection error measure E is as follows: E is commonly referred to as Sammon's stress. It is a measure of how well the interpattem distances are preserved when the patterns are projected from a higherdimensional space to a lower-dimensional space. The stress equal to 0 indicates a lossless mapping.

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Malerba, and 0 . 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.

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