2695 search results - page 244 / 539 » Learning Graph Matching |
134
Voted
ICML
2007
IEEE
16 years 4 months ago
2007
IEEE
The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
131
Voted
ICML
2007
IEEE
16 years 4 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...
142
Voted
ECML
2007
Springer
15 years 9 months ago
2007
Springer
The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some a...
152
Voted
ICML
2007
IEEE
16 years 4 months ago
2007
IEEE
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
146
Voted
ICML
2004
IEEE
16 years 4 months ago
2004
IEEE
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...