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» Graph parameters and semigroup functions
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ICML
2003
IEEE
14 years 8 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty
ICML
2009
IEEE
14 years 8 months ago
Learning spectral graph transformations for link prediction
We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectru...
Andreas Lommatzsch, Jérôme Kunegis
HYBRID
2007
Springer
13 years 11 months ago
Qualitative Analysis of Nonlinear Biochemical Networks with Piecewise-Affine Functions
Abstract. Nonlinearities and the lack of accurate quantitative information considerably hamper modeling and system analysis of biochemical networks. Here we propose a procedure for...
M. W. J. M. Musters, Hidde de Jong, P. P. J. van d...
ALENEX
2010
161views Algorithms» more  ALENEX 2010»
13 years 8 months ago
Route Planning with Flexible Objective Functions
We present the first fast route planning algorithm that answers shortest paths queries for a customizable linear combination of two different metrics, e. g. travel time and energy...
Robert Geisberger, Moritz Kobitzsch, Peter Sanders
ICASSP
2011
IEEE
12 years 11 months ago
Multi-sensor estimation and detection of phase-locked sinusoids
This paper proposes a method to compute the likelihood function for the amplitudes and phase shifts of noisily observed phase-locked and amplitude-constrained sinusoids. The sinus...
Christoph Reller, Hans-Andrea Loeliger, Stefano Ma...