.png){kind=icon}
.png){kind=icon}
Return
A practical use of regularization for supervised learning with kernel methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2013.01.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In several supervised learning applications, it happens that reconstruction methods have to be applied repeatedly before being able to achieve the final solution. In these situations, the availability of learning algorithms able to provide effective predictors in a very short time may lead to remarkable improvements in the overall computational requirement. In this paper we consider the kernel ridge regression problem and we look for solutions given by a linear combination of kernel functions plus a constant term. In particular, we show that the unknown coefficients of the linear combination and the constant term can be obtained very fastly by applying specific regularization algorithms directly to the linear system arising from the Empirical Risk Minimization problem. From the numerical experiments carried out on benchmark datasets, we observed that in some cases the same results achieved after hours of calculations can be obtained in few seconds, thus showing that these strategies are very well-suited for time-consuming applications. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Regularization algorithms
Kernel methods
Support vector machines
Conjugate gradient
Inverse problems
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W