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- W2770123274 abstract "Model-based classification of sequence data using a set of hidden Markov models is a well-known technique. The involved score function, which is often based on the class-conditional likelihood, can, however, be computationally demanding, especially for long data sequences. Inspired by recent theoretical advances in spectral learning of hidden Markov models, we propose a score function based on third-order moments. In particular, we propose to use the Kullback-Leibler divergence between theoretical and empirical third-order moments for classification of sequence data with discrete observations. The proposed method provides lower computational complexity at classification time than the usual likelihood-based methods. In order to demonstrate the properties of the proposed method, we perform classification of both simulated data and empirical data from a human activity recognition study." @default.
- W2770123274 created "2017-12-04" @default.
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- W2770123274 date "2018-01-01" @default.
- W2770123274 modified "2023-09-23" @default.
- W2770123274 title "Sequence Classification Using Third-Order Moments" @default.
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- W2770123274 doi "https://doi.org/10.1162/neco_a_01033" @default.
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