976 search results - page 16 / 196 » Training of Support Vector Machines with Mahalanobis Kernels |
EMNLP
2009
14 years 7 months ago
2009
We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. ...
NIPS
2001
14 years 10 months ago
2001
A novel approach for comparing sequences of observations using an explicit-expansion kernel is demonstrated. The kernel is derived using the assumption of the independence of the ...
PKDD
2009
Springer
15 years 4 months ago
2009
Springer
A serious drawback of kernel methods, and Support Vector Machines (SVM) in particular, is the difficulty in choosing a suitable kernel function for a given dataset. One of the appr...
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
AAAI
2006
14 years 11 months ago
2006
One of the well known risks of large margin training methods, such as boosting and support vector machines (SVMs), is their sensitivity to outliers. These risks are normally mitig...