Polynomial chaos theory (PCT) has been proven to be an efficient and effective way to represent and propagate uncertainty through system models and algorithms in general. In partic...
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
We study the problem of computing query results with confidence values in ULDBs: relational databases with uncertainty and lineage. ULDBs, which subsume probabilistic databases, o...
Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, m...
Hoyoung Jeung, Qing Liu, Heng Tao Shen, Xiaofang Z...