In this work, we study the problem of within-network relational learning and inference, where models are learned on a partially labeled relational dataset and then are applied to ...
Computer programs that can be expressed in two or more dimensions are typically called visual programs. The underlying theories of visual programming languages involve graph gramm...
Keven Ates, Jacek P. Kukluk, Lawrence B. Holder, D...
The efficacy of Anomaly Detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic v...
Gabriela F. Cretu, Angelos Stavrou, Michael E. Loc...
Personalized search systems have evolved to utilize heterogeneous features including document hyperlinks, category labels in various taxonomies and social tags in addition to free...
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is ...