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

invigorating

书目名称Discovery Science影响因子(影响力)




书目名称Discovery Science影响因子(影响力)学科排名




书目名称Discovery Science网络公开度




书目名称Discovery Science网络公开度学科排名




书目名称Discovery Science被引频次




书目名称Discovery Science被引频次学科排名




书目名称Discovery Science年度引用




书目名称Discovery Science年度引用学科排名




书目名称Discovery Science读者反馈




书目名称Discovery Science读者反馈学科排名

老巫婆

Model Tree Ensembles for Modeling Dynamic Systems,ally converted into a classical regression problem, which can then be solved with any nonlinear regression approach. As tree ensembles are a very successful predictive modelling approach, we investigate the use of tree ensembles for regression for this task..While ensembles of regression trees have

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Fast and Scalable Image Retrieval Using Predictive Clustering Trees,ficient and accurate systems for image retrieval. State-of-the-art systems for image retrieval use the bag-of-visual-words representation of the images. However, the computational bottleneck in all such systems is the construction of the visual vocabulary (i.e., how to obtain the visual words). This

Tdd526

Avoiding Anomalies in Data Stream Learning, refer to data cleaning as a pre-processing before the learning task. The problem of data cleaning is exacerbated when learning in the computational model of data streams. In this paper we present a streaming algorithm for learning classification rules able to detect contextual anomalies in the data

Freeze

Generalizing from Example Clusters,t with the given clusters. This is essentially a semi-supervised clustering problem, but it differs from previously studied semi-supervised clustering settings in significant ways. Earlier work has shown that none of the existing methods for semi-supervised clustering handle this problem well. We id

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A New Approach to String Pattern Mining with Approximate Match,ring data analysis on strings such as texts, word sequences, and genome sequences. The problem becomes difficult if the string patterns are allowed to match approximately, i.e., a fixed number of errors leads to an explosion in the number of small solutions, and a fixed ratio of errors violates the

NAVEN

OntoDM-KDD: Ontology for Representing the Knowledge Discovery Process,-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

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