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» Evaluating algorithms that learn from data streams
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SIGIR
2008
ACM
15 years 7 months ago
Modeling expert finding as an absorbing random walk
We introduce a novel approach to expert finding based on multi-step relevance propagation from documents to related candidates. Relevance propagation is modeled with an absorbing ...
Pavel Serdyukov, Henning Rode, Djoerd Hiemstra
CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 7 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
277
Voted
VLDB
2009
ACM
130views Database» more  VLDB 2009»
16 years 7 months ago
Multi-dimensional top-k dominating queries
Abstract The top-k dominating query returns k data objects which dominate the highest number of objects in a dataset. This query is an important tool for decision support since it ...
Man Lung Yiu, Nikos Mamoulis
221
Voted
PAMI
2010
238views more  PAMI 2010»
15 years 6 months ago
Tracking Motion, Deformation, and Texture Using Conditionally Gaussian Processes
—We present a generative model and inference algorithm for 3D nonrigid object tracking. The model, which we call G-flow, enables the joint inference of 3D position, orientation, ...
Tim K. Marks, John R. Hershey, Javier R. Movellan
APGV
2004
ACM
176views Visualization» more  APGV 2004»
16 years 29 days ago
Towards perceptually realistic talking heads: models, methods and McGurk
Motivated by the need for an informative, unbiased and quantitative perceptual method for the development and evaluation of a talking head we are developing, we propose a new test...
Darren Cosker, Susan Paddock, A. David Marshall, P...