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ECAI
2004
Springer
15 years 9 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
AEI
2010
115views more  AEI 2010»
15 years 3 months ago
BRAHMS: Novel middleware for integrated systems computation
Abstract-- Computational modellers are becoming increasingly interested in building large, eclectic, biological models. These may integrate nervous system components at various lev...
Benjamin Mitchinson, Tak-Shing Chan, Jonathan M. C...
ICMLA
2009
15 years 1 months ago
Sensitivity Analysis of POMDP Value Functions
In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. The...
Stéphane Ross, Masoumeh T. Izadi, Mark Merc...
ICCV
2011
IEEE
14 years 3 months ago
Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data
Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of t...
Gustavo Carneiro, Jacinto C. Nascimento
CCE
2005
15 years 3 months ago
CFD analyses of complex flows
Computational fluid dynamics (CFD) of complex processes and complicated geometries embraces the transport of momentum, heat, and mass including the description of reaction kinetic...
Richard Farmer, Ralph Pike, Gary Cheng