DTW-Distance Based Kernel for Time Series Data
نویسندگان
چکیده
منابع مشابه
Discrimination of time series based on kernel method
Classical methods in discrimination such as linear and quadratic do not have good efficiency in the case of nongaussian or nonlinear time series data. In nonparametric kernel discrimination in which the kernel estimators of likelihood functions are used instead of their real values has been shown to have good performance. The misclassification rate of kernel discrimination is usually less than ...
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For classification of time series, the simple 1-nearest neighbor (1NN) classifier in combination with an elastic distance measure such as Dynamic Time Warping (DTW) distance is considered superior in terms of classification accuracy to many other more elaborate methods, including k-nearest neighbor (kNN) with neighborhood size k > 1. In this paper we revisit this apparently peculiar relationshi...
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1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [email protected] (X.G.); [email protected] (G.L.); [email protected] (Q.L.) 2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China 3 Key Laboratory of Eco...
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1-Nearest Neighbor with the Dynamic TimeWarping (DTW) distance is one of the most effective classifiers on time series domain. Since the global constraint has been introduced in speech community, many global constraint models have been proposed including Sakoe-Chiba (S-C) band, Itakura Parallelogram, and Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint model that can...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2009
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e92.d.51