Xianping Ge
Padhraic Smyth
- 1.R. Agrawal, C. Faloutsos, and A. Swami. Efficient similarity search in sequence databases. In Proc. of the gth International Conference on Foundations of Data Organization and Algorithms, pages 69-84, Oct 1993. Google Scholar
Digital Library
- 2.R. Agrawal, K.-I. Lin, H. S. Sawhney, and K. Shim. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In VLDB, pages 490-501, Sep 1995. Google ScholarDigital Library
- 3.Y. Amit, U. Grenander, and M. Piccioni. Structural image restoration through deformable templates. Journal of the American Statistical Association, 86:376-387, 1991.Google ScholarCross Ref
- 4.D. J. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. In KDD-9g: AAAI Workshop on Knowledge Discovery in Databases, pages 359-370, July 1994.Google Scholar
- 5.K.-P. Chan and A. W.-C. Fu. Efficient time series matching by wavelets. In Proceedings 15th International Conference on Data Engineering, pages 126-133, Mar 1999. Google ScholarDigital Library
- 6.G. Das, K. Lin, H. Mannila, G. Rengenathan, and P. Smyth. Rule discovery from time series. In Proceedings of the 1998 Conference on Knowledge Discovery and Data Mining, pages 16-22. AAAI Press, 1998.Google Scholar
- 7.C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In SIGMOD-Proeeedings of Annual Conference, pages 419-429, May 1994. Google ScholarDigital Library
- 8.J. D. Ferguson. Variable duration models for speech. In Proc. Symposium on the Application of Hidden Markov Models to Text and Speech, pages 143-179, Oct 1980.Google Scholar
- 9.W. J. Holmes and M. J. Russell. Probabilistic-trajecory segmental HMMs. Computer Speech and Language, 13:3-37, 1999.Google ScholarDigital Library
- 10.Y.-W. Huang and P. S. Yu. Adaptive query processing for time-series data. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 282-286, 1999. Google ScholarDigital Library
- 11.H. Imai and M. Iri. An optimal algorithm for approximating a piecewise linear function. Journal of Information Processing, 9(3):59-62, 1986.Google Scholar
- 12.E. Keogh and P. Smyth. A probabilistic approach to fast pattern matching in time series databases. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining - KDD 97, pages 24-30, Aug 1997.Google ScholarDigital Library
- 13.E. J. Keogh and M. J. Pazzani. An indexing scheme for fast similarity search in large time series databases. In Proc. Eleventh International Conference on Scientific and Statistical Database Management, pages 56-67, Jul 1999. Google ScholarDigital Library
- 14.D. M. Manos and D. L. Flamm, editors. Plasma Etching An Introduction. Academic Press, Inc., San Diego, 1989.Google Scholar
- 15.K. V. Mardia and I. L. Dryden. Statistical Shape Analysis. John Wiley & Sons, Ltd, 1998.Google Scholar
- 16.D. B. Percival and A. T. Vralden. Wavelet Methods for Time Series Analysis. Cambridge University Press, 2000.Google Scholar
- 17.H. Shatkay and S. B. Zdonik. Approximate queries and representations for large data sequences. In Proceedings of the Twelfth International Conference on Data Engineering, pages 536-545, Feb 1996. Google ScholarDigital Library
- 18.P. F. Williams, editor. Plasma Processing of Semiconductors. Kuwer Academic Publishers, 1997.Google ScholarCross Ref
- 19.J. G. Wilpon, L. R. Rabiner, C.-H. Lee, and E. R. Goldman. Automatic recognition of keywords in unconstrained speech using hidden markov models. IEEE Transactions on Acoustics Speech and Signal Processing, 38(11):1870-1878, Nov 1990.Google ScholarCross Ref
- 20.B.-K. Yi, H. V. Jagadish, and C. Faloutsos. Efficient retrieval of similar time sequences under time warping. In Proceedings. 14th International Conference on Data Engineering, pages 201-208, Feb 1998. Google ScholarDigital Library
- 21.Y. Zhu and L. D. Seneviratne. Optimal polygonal approximation of digitized curves. IEE proceedings. Vision image and signal processing, 144(1):8-14, Feb 1997.Google Scholar
Index Terms
- Deformable Markov model templates for time-series pattern matching
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