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TIM
2010
144views Education» more  TIM 2010»
15 years 1 months ago
Extending Polynomial Chaos to Include Interval Analysis
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...
Antonello Monti, Ferdinanda Ponci, Marco Valtorta
PAMI
2012
13 years 9 months ago
Task-Driven Dictionary Learning
—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...
Julien Mairal, Francis Bach, Jean Ponce
BMCBI
2004
208views more  BMCBI 2004»
15 years 6 months ago
Using 3D Hidden Markov Models that explicitly represent spatial coordinates to model and compare protein structures
Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
Vadim Alexandrov, Mark Gerstein
ICDE
2008
IEEE
160views Database» more  ICDE 2008»
16 years 8 months ago
Exploiting Lineage for Confidence Computation in Uncertain and Probabilistic Databases
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...
Anish Das Sarma, Martin Theobald, Jennifer Widom
ICDE
2008
IEEE
301views Database» more  ICDE 2008»
16 years 8 months ago
A Hybrid Prediction Model for Moving Objects
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...