Kernel Machines for Continuous and Discrete Variables
نویسندگان
چکیده
Kernel Machines, such as Support Vector Machines, have been frequently used, with considerable success, in situations in which the input variables were real values. Lately, these methods have also been extended to deal with discrete data such as string characters, microarray gene expressions, biosequences, etc. In this contribution we describe a new kernel allowing kernel machines to be applied in problems in which continuous and discrete variables, described mainly by its order of magnitude, but also by an interval, take part simultaneously. In addition, the structure of the features space induced by this kernel is also defined considering the nature of both continuous and discrete variables.
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