Titlebook: Discovery Science; 12th International C Jo?o Gama,Vítor Santos Costa,Pavel B. Brazdil Conference proceedings 2009 Springer-Verlag Berlin He

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書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學(xué)科排名




書目名稱Discovery Science網(wǎng)絡(luò)公開度




書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discovery Science被引頻次




書目名稱Discovery Science被引頻次學(xué)科排名




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書目名稱Discovery Science年度引用學(xué)科排名




書目名稱Discovery Science讀者反饋




書目名稱Discovery Science讀者反饋學(xué)科排名

Repetitions

Measuring Scientific Reasoning Competenciesbasis of the node links. We propose a regression inference procedure that is based on a co-training approach according to separate model trees are learned from both attribute values of labeled nodes and attribute values aggregated in the neighborhood of labeled nodes, respectively. Each model tree i

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Modeling the Student in Sherlock II on all data sets. We show that a substantial improvement in performance is obtained using an ensemble of MICCLLR classifiers trained using different base learners. We also show that an extra gain in classification accuracy is obtained by applying AdaBoost.M1 to weak MICCLLR classifiers. Overall, ou

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Formal Approaches to Student Modelling global model adaptation. The adaptation strategy consists of building a new tree whenever a change is suspected in the region and replacing the old ones when the new trees become more accurate. This enables smooth and granular adaptation of the global model. The results from the empirical evaluatio

設(shè)施

Farideh Salili,Chi Yue Chiu,Ying Yi Hongstage density-based clustering for the .-partite graph constructed from the 1st-stage density-based clustering result for each timestamp network. For a given data set, CHRONICLE finds all clusters in a fixed time by using a fixed amount of memory, regardless of the number of clusters and the length

Terminal

The Culture and Context of Learning logical and relational space we introduce a low dimensional embedding method. The technique is amenable to skewed/non-skewed class distribution where multi-class problems such as protein fold recognition are generally characterized by highly uneven class distribution. We performed a series of exper

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