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Abstract : |
In this ' contribution we describe a novel classification approach for on-line handwriting recognition. The technique combines dynamic time warping (DTW) and support vector machines (SVMs) by establishing a new SVM kernel. We call this ' kernel Gaussian DTW (GDTW) ker-nel. This kernel approach haw ' a main advantage over common HMM techniques. It does not assume a model for the generarive class conditional densities. Instead, it directly addresses the problem of discrimination by creating class boundaries and thus is ' less sensitive to modeling assumptions. By incorporating DTW in the kernel function, general classification problems with variable-sized sequential data can be handled. In this respect the proposed method can be straightforwardly applied to all classification problems, where DTW gives a reasonable distance measure, e.g. speech recognition or genome processing. We show experiments with this ' kernel approach on the UNIPEN handwriting data, achieving results ' comparable to an HMMbased technique., |
