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...
Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A variety of kernel learning algorithms have been prop...
The problem of obtaining the maximum a posteriori (map) estimate of a discrete random field is of fundamental importance in many areas of Computer Science. In this work, we build ...
In this paper, we explore the problems associated with exception handling from a new dimension: the human. We designed a study that evaluates (1) different perspectives of softwar...
The evolution of the Web requires to consider an increasing number of context-dependency issues. Therefore, in our research we focus on how to extend a Web application with additi...