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» Kernels and Regularization on Graphs
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JMLR
2008
95views more  JMLR 2008»
15 years 6 months ago
Learning Similarity with Operator-valued Large-margin Classifiers
A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifie...
Andreas Maurer
ICML
2009
IEEE
16 years 7 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
FCCM
2006
IEEE
113views VLSI» more  FCCM 2006»
16 years 11 days ago
GraphStep: A System Architecture for Sparse-Graph Algorithms
— Many important applications are organized around long-lived, irregular sparse graphs (e.g., data and knowledge bases, CAD optimization, numerical problems, simulations). The gr...
Michael DeLorimier, Nachiket Kapre, Nikil Mehta, D...
AUTOMATICA
2010
167views more  AUTOMATICA 2010»
15 years 6 months ago
A new kernel-based approach for linear system identification
This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose s...
Gianluigi Pillonetto, Giuseppe De Nicolao
PAMI
2010
337views more  PAMI 2010»
15 years 4 months ago
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
Kwang In Kim, Younghee Kwon