A new metric for dissimilarity data classification based on Support Vector Machines optimization
Résumé
Dissimilarities are extremely useful in many real-world pattern classification problems, where the data resides in a complicated, complex space, and it can be very difficult, if not impossible, to find useful feature vector representations. In these cases a dissimilarity representation may be easier to come by. The goal of this work is to provide a new technique based on Support Vector Machines (SVM) optimization that can be a good alternative in terms of accuracy compared to known methods using dissimilarities such as k nearest neighbor classifier (kNN), prototype-based dissimilarity classifiers and distance kernel based SVM classifiers.