93 search results - page 9 / 19 » Maximal Discrepancy for Support Vector Machines |
NECO
2008
13 years 7 months ago
2008
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
PKDD
2009
Springer
14 years 1 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...
ICDM
2007
IEEE
13 years 11 months ago
2007
IEEE
Many data mining applications can benefit from adapting existing classifiers to new data with shifted distributions. In this paper, we present Adaptive Support Vector Machine (Ada...
ROCAI
2004
Springer
14 years 21 days ago
2004
Springer
For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems ...
ICONIP
2007
13 years 8 months ago
2007
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...