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ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 5 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ECAI
2004
Springer
16 years 1 days 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
VIS
2009
IEEE
300views Visualization» more  VIS 2009»
16 years 7 months ago
Predictor-Corrector Schemes for Visualization of Smoothed Particle Hydrodynamics Data
Abstract--In this paper we present a method for vortex core line extraction which operates directly on the smoothed particle hydrodynamics (SPH) representation and, by this, genera...
Benjamin Schindler, Raphael Fuchs, John Biddisco...
ICPR
2000
IEEE
16 years 7 months ago
Piecewise Linear Skeletonization Using Principal Curves
We propose an algorithm to find piecewise linear skeletons of hand-written characters by using principal curves. The development of the method was inspired by the apparent similar...
Adam Krzyzak, Balázs Kégl
TLDI
2010
ACM
247views Formal Methods» more  TLDI 2010»
16 years 3 months ago
F-ing modules
ML modules are a powerful language mechanism for decomposing programs into reusable components. Unfortunately, they also have a reputation for being “complex” and requiring fa...
Andreas Rossberg, Claudio V. Russo, Derek Dreyer