By Mircea Negoita, Robert J. Howlett, Lakhmi C. Jain (Editors)
The three-volume set LNAI 3213, LNAI 3214, and LNAI 3215 constitutes the refereed lawsuits of the eighth foreign convention on Knowledge-Based clever details and Engineering structures, KES 2004, held in Wellington, New Zealand in September 2004. The over 450 papers offered have been conscientiously reviewed and chosen from quite a few submissions. The papers current a wealth of unique learn effects from the sector of clever details processing within the broadest feel; one of the parts lined are man made intelligence, computational intelligence, cognitive applied sciences, smooth computing, facts mining, wisdom processing, numerous new paradigms in biologically encouraged computing, and purposes in a variety of domain names like bioinformatics, finance, sign processing and so forth.
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Extra resources for Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Part II
Jefferies, Michael C. Cosgrove, Jesse T. Baker, Wai-Kiang Yeap 232 XXXVIII Table of Contents, Part I Intelligent Tutoring Systems Developing a “Virtual Student” Model to Test the Tutor and Optimizer Agents in an ITS Mircea Gh. T. Goh, Kinshuk 277 Ontologies for Creating Learning Object Content 284 PASS: An Expert System with Certainty Factors for Predicting Student Success Ioannis Hatzilygeroudis, Anthi Karatrantou, C. D. D. Syed Mustapha 350 Gallery: In Support of Human Memory Hung-Hsuan Huang, Yasuyuki Sumi, Toyoaki Nishida 357 Evaluation of the Communication Atmosphere Tomasz M.
Kadirkamanathan, HA Thompson, PJ Fleming 699 Representing Knowledge in Controlled Natural Language: A Case Study Rolf Schwitter 711 Supporting Smart Applications in Multihop Ad-Hoc Networks The GecGo Middleware Peter Sturm, Hannes Frey, Daniel Gšrgen, Johannes Lehnert 718 A Heuristic for Efficient Broadcasting in the Metropolitan Ad hoc Networks Luc Hogie, Frederic Guinand, Pascal Bouvry 727 ADS as Information Management Service in an M-Learning Environment Matthias R. Brust, Daniel Görgen, Christian Hutter, Steffen Rothkugel 734 Table of Contents, Part I XLIII Rough Sets - Theory and Applications Noise Reduction in Audio Employing Spectral Unpredictability Measure and Neural Net Andrzej Czyzewski, Marek Dziubinski 743 Forming and Ranking Musical Rhythm Hypotheses Bozena Kostek, Jaroslaw Wojcik 750 A Comparison of Two Approaches to Data Mining from Imbalanced Data Jerzy W.
1 Kohonen’s LVQ Prediction Technique The basic feature of LVQ method is we could add new training vectors at typical situations to improve the performance of individual neighbourhoods within the map. Table 1 shows sample of the selling behaviour patterns used as training data set for learning the fuzzy behavioral categories. This training data set shows pruned scores in four selling behaviour categories. The pruned score have been derived from heuristics used by an expert system model developed earlier .