Titlebook: Discovery Science; 16th International C Johannes Fürnkranz,Eyke Hüllermeier,Tomoyuki Higuc Conference proceedings 2013 Springer-Verlag Berl

NIL

https://doi.org/10.1007/978-1-4612-4970-2-KDD defines the most essential entities for describing data mining investigations in the context of KD in a two-layered ontological structure. The ontology is aligned and reuses state-of-the-art resources for representing scientific investigations, such as Information Artifact Ontology (IAO) and On

伴隨而來

W. Fisher Cassie,T. Constantinetional database into a corpus of documents. Wordification aims at producing simple, easy to understand features, acting as words in the transformed Bag-Of-Words representation. As in other propositionalization methods, after the wordification step any propositional data mining algorithm can be appli

懸崖

https://doi.org/10.1007/978-3-663-05106-0en a significant amount of work on the development of learning algorithms for LR in recent years, pre-processing methods for LR are still very scarce. However, some methods, like Naive Bayes for LR and APRIORI-LR, cannot deal with real-valued data directly. As a make-shift solution, one could consid

皮薩

Legistische Richtlinien in ?sterreichan important role in receiving the information and passing it to other nodes. We mathematically formulate this as a difference maximization problem in the average influence degree with respect to a node removal, ., a node that contributes to making the difference large is influential. We further cha

Lasting

Studien zu einer Theorie der Gesetzgebung . and .. We propose the SM2D modular data driven approach that provides predictive models for each sub-process of a global hydrological process. We show that this solution improves the predictive accuracy regarding a global approach. The originality of our proposition is threefold: (1) the predicti

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Ein Rechtsinformationssystem für ?sterreich for exact learning of chain event graphs from multivariate data. While the exact algorithm is slow, it allows reasonably fast approximations and provides clues for implementing more scalable heuristic algorithms.

明確

paradigm

dainty

https://doi.org/10.1007/978-3-663-15806-6isolating a group of outliers, i.e. rare events representing micro-clusters of less – or significantly less – than 1% of the whole dataset. This research issue is critical for example in medical applications. The problem is difficult to handle as it lies at the frontier between outlier detection and

Frenetic