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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Classification of Temporal Data Based on Self-organizing Incremental Neural Network
verfasst von : Shogo Okada, Osamu Hasegawa
Erschienen in: Artificial Neural Networks – ICANN 2007
Verlag: Springer Berlin Heidelberg
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This paper presents an approach (SOINN-DTW) for recognition of temporal data that is based on Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping. Using SOINN’s function that eliminates noise in the input data and represents topological structure of input data, SOINN-DTW method approximates output distribution of each state and is able to construct robust model for temporal data. SOINN-DTW method is the novel method that enhanced Stochastic Dynamic Time Warping Method (Nakagawa,1986). To confirm the effectiveness of SOINN-DTW method, we present an extensive set of experiments that show how our method outperforms HMM and Stochastic Dynamic Time Warping Method in classifying phone data and gesture data.