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» Combining Simple Models to Approximate Complex Dynamics
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ICML
2005
IEEE
14 years 8 months ago
Learning as search optimization: approximate large margin methods for structured prediction
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., li...
Daniel Marcu, Hal Daumé III
NIPS
2001
13 years 9 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
JMLR
2008
151views more  JMLR 2008»
13 years 7 months ago
Learning to Combine Motor Primitives Via Greedy Additive Regression
The computational complexities arising in motor control can be ameliorated through the use of a library of motor synergies. We present a new model, referred to as the Greedy Addit...
Manu Chhabra, Robert A. Jacobs
ICCV
2011
IEEE
12 years 7 months ago
Decision Tree Fields
This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fiel...
Sebastian Nowozin, Carsten Rother, Shai Bagon, Ban...
COMCOM
2010
167views more  COMCOM 2010»
13 years 5 months ago
Effective load for flow-level performance modelling of file transfers in wireless LANs
Today, a wide range of 802.11-based Wireless LANs (WLANs) have become dominant to provide wireless Internet access for file transfers. For engineering purposes, there is a need fo...
Geert Jan Hoekstra, Robert D. van der Mei