New Jaccard-Distance Based Support Vector Machine Kernel for Handwritten Digit Recognition

This paper proposes a new negative Jaccard distance- based kernel for Support Vector Machines (SVM). The Jaccard distance is based on shape comparison between data, which could have a particular importance for handwritten character recognition where each class has its own shape form. So, it seems more proficient than Euclidian distance that is used with conventional kernels. The performance of negative Jaccard kernel is evaluated comparatively to standard SVM kernels for handwritten digit recognition. Experiments are conducted on both One-Against-All (OAA) and One-Against-One (OAO) multi-class SVM implementations using samples taken from USPS database. The results obtained showed that Jaccard Negative Distance kernel outperforms other kernels in most cases.