Titlebook: Research and Development in Intelligent Systems XXIX; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2012 Springer-

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A Hybrid Model for Business Process Event Predictionhe data extracted from a combination of historical (closed) and/or live (open) workflows (jobs or process instances). In real-world applications, the problem is compounded by the fact that the number of unique workflows (process prototypes) can be enormous, their occurrences can be limited, and a re

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Biologically inspired speaker verification using Spiking Self-Organising Mapted using 50 speakers from the Centre for Spoken Language Understanding (CSLU2002) speaker verification database and shows a speaker verification performance of 90.1%. This compares favorably with previous non-spiking self organising map that used Discrete Fourier Transform (DFT)-based input feature vector with the same dataset.

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Questionnaire Free Text Summarisation Using Hierarchical Classificationch is evaluated using free text from questionnaires used in the SAVSNET (Small Animal Veterinary Surveillance Network) project. The results demonstrate the viability of using hierarchical classification to generate free text summaries.

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S-Theta: low steering path-planning algorithmalgorithm significantly reduces the heading changes, in both, indoors and outdoors problems as results show, making the algorithm especially suitable for robots whose ability to turn is limited or the associated cost is high.

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A Geometric Moving Average Martingale method for detecting changes in data streamsed on the GMAM value. Once a change is detected, then all variables of the GMAM algorithm are re-initialized in order to find other changes. The experiments show that the GMAM method is effective in detecting concept changes in two synthetic time-varying data streams and a real world dataset ‘Respiration dataset’.

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Using Chunks to Categorise Chess Positionsd retrieved. We extend an implementation of the template theory, CHREST, to support the learning and retrieval of categorisations of chess positions. Our extended model provides equivalent performance to a support-vector machine in categorising chess positions by opening, and reveals how learning for retrieval relates to learning for content.

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