11 search results - page 2 / 3 » Nonparametric Transforms of Graph Kernels for Semi-Supervise... |
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Som...
PKDD
2010
Springer
13 years 2 months ago
2010
Springer
Abstract. The eigenspectrum of a graph Laplacian encodes smoothness information over the graph. A natural approach to learning involves transforming the spectrum of a graph Laplaci...
PKDD
2009
Springer
13 years 11 months ago
2009
Springer
Abstract. Gaussian processes have successfully been used to learn preferences among entities as they provide nonparametric Bayesian approaches for model selection and probabilistic...
ICPR
2006
IEEE
14 years 5 months ago
2006
IEEE
We present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. The approach is based on building a ke...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
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...