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IJAR
2006
89views more  IJAR 2006»
15 years 6 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
SIBGRAPI
2006
IEEE
16 years 14 days ago
Actively Illuminated Objects using Graph-Cuts
This paper addresses the problem of foreground extraction using active illumination and graph-cut optimization. Our approach starts by detecting image regions that are likely to b...
Asla Medeiros Sá, Marcelo Bernardes Vieira,...
CORR
2006
Springer
127views Education» more  CORR 2006»
15 years 6 months ago
Approximating Rate-Distortion Graphs of Individual Data: Experiments in Lossy Compression and Denoising
Classical rate-distortion theory requires knowledge of an elusive source distribution. Instead, we analyze rate-distortion properties of individual objects using the recently devel...
Steven de Rooij, Paul M. B. Vitányi
ENDM
2006
70views more  ENDM 2006»
15 years 6 months ago
Quasirandomness in Graphs
Jim Propp's rotor router model is a simple deterministic analogue of a random walk. Instead of distributing chips randomly, it serves the neighbors in a fixed order. We analy...
Benjamin Doerr, Tobias Friedrich
WWW
2010
ACM
16 years 1 months ago
Tracking the random surfer: empirically measured teleportation parameters in PageRank
PageRank computes the importance of each node in a directed graph under a random surfer model governed by a teleportation parameter. Commonly denoted alpha, this parameter models ...
David F. Gleich, Paul G. Constantine, Abraham D. F...