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

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宽宏大量

An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting,basis 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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画布

炼油厂

MICCLLR: Multiple-Instance Learning Using Class Conditional Log Likelihood Ratio, 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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Regression Trees from Data Streams with Drift Detection, 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

ACTIN

CHRONICLE: A Two-Stage Density-Based Clustering Algorithm for Dynamic Networks,stage 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

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musicologist

Unsupervised Fuzzy Clustering for the Segmentation and Annotation of Upwelling Regions in Sea Surfaber of clusters providing an effective segmentation of the SST images whose spatial visualization of fuzzy membership closely reproduces the original images. Comparing the AP-FCM with the FCM using several validation indices shows the advantage of the AP-FCM avoiding under or over-segmented images.