The nature of statistical learning theory
The nature of statistical learning theory
On-Line Handwriting Recognition with Support Vector Machines " A Kernel Approach
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning HMM Structure for On-Line Handwriting Modelization
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Model-based kernel for efficient time series analysis
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper proposes a new approach for classifying multivariate time-series with applications to the problem of writer independent online handwritten character recognition. Each time-series is approximated by a sum of piecewise polynomials in a suitably defined Reproducing Kernel Hilbert Space (RKHS). Using the associated kernel function a large margin classification formulation is proposed which can discriminate between two such functions belonging to the RKHS. The associated problem turns out to be an instance of convex quadratic programming. The resultant classification scheme applies to many time-series discrimination tasks and shows encouraging results when applied to online handwriting recognition tasks.