论文信息 - A new time series classification approach

A new time series classification approach

Abstract A new, robust and computationally attractive approach to the problem of time series classification is discussed in this paper. Both the Bayesian as well as a new adaptive classification scheme for source selection are discussed. Simulation results are included to demonstrate the effectiveness of the new methodology.

Konstantinos N. Plataniotis | Anastasios N. Venetsanopoulos | Demetrios G. Lainiotis | Dimitrios Androutsos

[1] Demetrios G. Lainiotis,et al. Optimal Estimation in the Presence of Unknown Parameters , 1969, IEEE Trans. Syst. Sci. Cybern..

[2] Demetrios G. Lainiotis,et al. Adaptive deconvolution of seismic signals-performance, computational analysis, parallelism , 1988, IEEE Trans. Acoust. Speech Signal Process..

[3] Athanasios Kehagias,et al. Modular neural networks for MAP classification of time series and the partition algorithm , 1996, IEEE Trans. Neural Networks.

[4] Pietro Burrascano,et al. A norm selection criterion for the generalized delta rule , 1991, IEEE Trans. Neural Networks.

[5] Athanasios Kehagias,et al. A Recurrent Network Implementation of Time Series Classification , 1996, Neural Computation.

[6] Konstantinos N. Plataniotis. Distributed parallel processing state estimation algorithms , 1994 .

[7] Demetrios G. Lainiotis,et al. Passive tracking of a maneuvering target: an adaptive approach , 1994, IEEE Trans. Signal Process..

[8] Ioannis Pitas,et al. Nonlinear Digital Filters - Principles and Applications , 1990, The Springer International Series in Engineering and Computer Science.