PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Supervised Selective Combining Pattern Recognition Modalities and its Application to Signature Verification by Fusing On-Line and Off-Line Kernels
Alexander Tatarchuk, Valentina Sulimova, David Windridge and Vadim Mottl
In: 8th International Workshop on Multiple Classifier Systems MCS 2009, Reykjavik(2009).

Abstract

We consider the problem of multi-modal pattern recognition under the assumption that a kernel-based approach is applicable within each particular modality. The Cartesian product of the linear spaces into which the respective kernels embed the output scales of single sensors is employed as an appropriate joint scale corresponding to the idea of combining modalities at the sensor level. This contrasts with the commonly adopted method of combining classifiers inferred from each specific modality. However, a significant risk in combining linear spaces is that of overfitting. To address this, we set out a stochastic method for encompassing modal-selectivity that is intrinsic to (that is to say, theoretically contiguous with) the selected kernel-based pattern-recognition approach. The principle of kernel selectivity supervision is then applied to the problem of signature verification by fusing several on-line and off-line kernels into a complete training and verification technique.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6565
Deposited By:David Windridge
Deposited On:08 March 2010