Applications of support vector machine regression in metrology and data fusion

Support vector machine (SVM) regression is a new tool for the approximation of a data set comprising of two or more sources of data of the same type but with diierent noise levels. These types of data set occur frequently in metrology, e.g., when measurements from coordinate measuring machines (CMMs) of diiering precisions are merged. More generally, these data sets are found in a variety of problems of \data fusion," the collective name given to situations in which we wish to combine data obtained from multiple and/or diierent sources. Therefore, SVM regression has potential applications in a number of metrology and data fusion problems. In this paper, we introduce the topic of SVM regression and discuss the generic types of data fusion problems that it can solve. We show that SVM regression in its most common form can be formulated mathematically as a quadratic programming problem. Furthermore, we consider how SVM regression can be extended to solve more general problems, as well as discussing when it is appropriate to use these general techniques.