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» Graph parameters and semigroup functions
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ICML
2003
IEEE
15 years 10 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
15 years 10 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
15 years 1 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»
14 years 11 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
14 years 1 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...