In the case of using the Mahalanobis distance as discriminant function, usually the covariance matrix calculated from training samples is used. However, it is extremely difficult to prepare enough training samples if the dimension of feature vector is large. Therefore, estimated eigenvalues and eigenvectors of covariance matrix will include errors that cause misclassification. In this paper, a ...

A relevant research issue in naturalistic studies is related to analysis and evaluation of the huge data recorded during the experiment. A procedure to identify critical events of PTW is presented and tested using parameters measured or derived by GPS. Raw measures of speed, position and time are copied directly from the data string stored by the GPS in National Marine Electronics Association (...

In data privacy, record linkage is a well known technique used to evaluate the disclosure risk of protected data. Mainly, the idea is the linkage between records of different databases, which make reference to the same individuals. In this paper we introduce a new parametrized variation of record linkage relying on the Mahalanobis distance, and a supervised learning method to determine the opti...

Support Vector Machine (SVM) is a powerful technique for data classification. The SVM constructs an optimal separating hyper-plane as a decision surface, to divide the data points of different categories in the vector space. The Kernel functions are used to extend the concept of the optimal separating hyper-plane for the non-linearly separable cases so that the data can be linearly separable. T...

Article history: Received 7 October 2007 Received in revised form 27 February 2008 Accepted 16 May 2008