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In this study, we propose an ensemble learning architecture called "Cognitive Learner", for classification of cognitive states from functional magnetic resonance imaging (fMRI). Proposed architecture consists of a two-layer hierarchy. In the first layer, called voxel layer, we model the connectivity among the voxel time series to represent the detailed information about the experiment. In...
Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system...
Nowadays, increasing demand on image annotation requirement increases the number of automatic image annotation systems. However, performances of current annotation methods are far from practical usage. Common problem of current methods is the gap between semantic words and low level visual descriptors. Because of semantic gap, annotation results of these methods contain irrelevant noisy words. To...
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