Asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces
作者：
Highlights：
• Asymmetric LVQ scheme for time series.
• Decision boundary defined by two prototypes is piecewise quadratic.
• Margin growth principle for LVQ methods in arbitrary distance spaces.
• Empirical comparison of prototype generation methods for NN classification.
• Asymmetric generalized LVQ best trades speed against accuracy.
摘要
•Asymmetric LVQ scheme for time series.•Decision boundary defined by two prototypes is piecewise quadratic.•Margin growth principle for LVQ methods in arbitrary distance spaces.•Empirical comparison of prototype generation methods for NN classification.•Asymmetric generalized LVQ best trades speed against accuracy.
论文关键词：Learning vector quantization,Time series,Dynamic time warping
论文评审过程：Received 26 May 2017, Revised 4 September 2017, Accepted 23 October 2017, Available online 7 November 2017, Version of Record 23 November 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.10.029
