Titlebook: Handbuch des Telekommunikationsrechts; Dragana Damjanovic,Michael Holoubek,Wolfgang Urban Book 2006Latest edition Springer-Verlag Vienna 2

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different degrees. Therefore, the more complex but informative label space makes it challenging to directly model the relationship between original features and label distributions. In this paper, an algorithm called Label Distribution Learning with Discriminative Instance Mapping (LDLDIM) is propo

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challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN, an unsupervised anomaly detection model for time-series that can learn complex temporal patterns automatically and generalize wel

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d for many years in the literature. Rule-based models are an important class of such models. However, most of the common algorithms for learning rule-based models rely on heuristic search strategies developed for specific rule-learning settings. These search strategies are very different from those

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ll number of meaningful clusters. However, there still exist several challenges for document clustering, such as high dimensionality, scalability, accuracy, meaningful cluster labels, and extracting semantics from texts. In order to improve the quality of document clustering results, we propose an e

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ostly focus on learning representations via characterizing the social structural balance theory in signed networks. However, structural balance theory could not well satisfy some of the fundamental phenomena in real-world signed networks such as the direction of links. As a result, in this paper we

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more application needs while unconstrained methods can only control the number of produced clusters. Thirdly, the proposed method is general and can be used to solve other practical constraints. The experimental studies on word grouping and result visualization show very encouraging results.

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