Offline handwritten signature verification using radial basis function neural network

This study investigates the effectiveness of Radial Basis Function Neural Networks (RBFNNs) for Of- fline Handwritten Signature Verification (OHSV). A signature database is collected using intrapersonal variations for evaluation. Global, grid and texture features are used as feature sets. A number of exper- iments were carried out to compare the effectiveness of each separate set and their combination. The system is extensively tested with random signature forgeries and the high recognition rates obtained demonstrate the effectiveness of the architecture. The best results are obtained when global and grid features are combined producing a feature vector of 592 elements. In this case a Mean Error Rate (MER) of 2.04% with a False Rejection Rate (FRR) of 1.58% and a False Acceptance Rate (FAR) of 2.5% are achieved, which are generally better than those reported in the literature.peer-reviewe

Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Cortical Models for Movement Control

Defense Advanced Research Projects Agency and Office of Naval Research (N0014-95-l-0409)