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Titlebook: Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects; 8th Industrial Confe Petra Perner Conference

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Additive and Abelian Categories,r which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. As selection method that combines relevance and redundancy we try out a Hopfield network. We experimentally compare selection methods, running unitary redund
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Masaki Kashiwara,Pierre Schapiraches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. However, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. Symbolic information helps users to efficiently analyse
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Additive and Abelian Categories,y to agriculture. Due to the use of sensors and GPS technology, in today’s agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. In this paper we de
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Masaki Kashiwara,Pierre Schapirancephalogram (EEG) signal. A set of features is extracted and classification techniques are then used to eventually derive an alarm signal predicting a coming seizure. The epileptic patient may then take steps in order to prevent accidents and social exposure.
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Additive and Abelian Categories,tool. The application domain we deal with is travel medicine, which combines medical aspects with geography, climate, holiday activities and associated traveling conditions. In this paper we present the application’s requirements and show how knowledge models can be developed using an industrial str
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