Titlebook: Research and Development in Intelligent Systems XIX; Proceedings of ES200 Max Bramer (Technical Programme Chair),Alun Preece Conference pro

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A Rough Set Model with Ontological Information for Discovering Maximal Association Rules in Documentnd compare its applicability with that of the maximal association method. Both methods are based on computing co-occurrences of various sets of keywords, but it has been shown that by using the Rough Set method, rules discovered are similar to maximal association rules, and it is much simpler than t

mutineer

Simplest Rules Characterizing Classes Generated by δ-Free Setsondensed representation (δ-free sets) of data which is efficiently computed. Produced rules have a minimal body (i.e. any subset of the left-hand side of a rule does not enable to conclude on the same class value). We show a sensible sufficient condition that avoids important classification conflict

聯(lián)想

Epicurean-style Learning Applied to the Classification of Gene-Expression Data, vol. 7, 2001, and Golub et al., ., vol. 286, 1999. The classification problem studied by Khan et al. is related to the diagnosis of small round blue cell tumours of childhood (SRBCT) which are difficult to classify both clinically and via routine histology. Golub et al. study acute myeloid leukemi

Opponent

forecast

Generating Context-Based Explanationstilised in the reasoning. A solution is thus to reproduce informal knowledge relating and complementing the formal one. The term “context-based explanations” is used for explanations based on formal and informal domain knowledge. An architecture generating such context-based explanations is describe

褻瀆

Facilitating DL-based Hybrid Reasoning with Inference Fusioneneous reasoners. Our approach integrates results from a DL-based system with results from a constraint solver under the direction of a global reasoning coordinator.. is performed by (i) processing heterogeneous input knowledge, producing suitable homogeneous input knowledge for each specialised rea

開玩笑

Representing and Eliciting “If … Then Rules”: An Empirical Analysisvestigates the relationship between using different, but equivalent knowledge representations for knowledge elicitation (e.g., “If — then” and “Given”), while focusing on probability knowledge. In addition, this paper investigates other issues that can influence the knowledge elicited. Tense (presen

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