Classification of Temporal Data Based on Self-organizing Incremental Neural Network
Classification of Temporal Data Based on Self-organizing Incremental Neural Network
复制标题
基于自组织增量神经网络的时态数据分类
DOI：
10.1007/978-3-540-74695-9_48
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
O. Hasegawa
中科院分区：
文献类型：
--
作者：
S. Okada;O. Hasegawa
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.
