  The nearest neighbor method together with the dynamic time warping (DTW)
distance is one of the most popular approaches in time series classification.
This method suffers from high storage and computation requirements for large
training sets. As a solution to both drawbacks, this article extends learning
vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ
scheme uses asymmetric weighted averaging as update rule. Empirical results
exhibited superior performance of asymmetric generalized LVQ (GLVQ) over other
state-of-the-art prototype generation methods for nearest neighbor
classification.

Machine Learning;Machine Learning