Invariant time-series classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7524 LNAI, Issue PART 2, 2012, Pages 725-740

  Grabocka, Josif ^a   Nanopoulos, Alexandros ^b   Schmidt Thieme, Lars ^a  

^a Information Systems and Machine Learning Lab   (Germany)

^b CATHOLIC UNIVERSITY OF EICHSTÄTT INGOLSTADT   (Germany)

Author keywords

Data Driven Transformations; Invariant Classification; Machine Learning; Time Series Classification

Indexed keywords

APPLICATION AREA; DATA SETS; DECISION BOUNDARY; DYNAMIC TIME WARPING; INTRA-CLASS VARIATION; MAXIMUM MARGIN; MOVING LEAST SQUARES; NEAREST NEIGHBORS; SUPPORT VECTOR; TIME SERIES CLASSIFICATIONS; TRAINING SETS; TRANSFORMATION METHODS;

DIAGNOSIS; EXPERIMENTS; LEARNING SYSTEMS; TIME SERIES;

SUPPORT VECTOR MACHINES;

EID: 84866843796     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33486-3_46     Document Type: Conference Paper

References (23)

1. Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications With R Examples. Springer (2011) ISBN 978-1-4419-7864-6
2. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: KDD, pp. 285-289 (2000)
3. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.J.: Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2), 1542-1552 (2008)
4. Gudmundsson, S., Runarsson, T.P., Sigurdsson, S.: Support vector machines and dynamic time warping for time series. In: IJCNN, pp. 2772-2776. IEEE (2008)
5. Schölkopf, B., Burges, C., Vapnik, V.: Incorporating invariances in support vector learning machines, pp. 47-52. Springer (1996)
6. DeCoste, D., Schölkopf, B.: Training invariant support vector machines. Machine Learning 46(1-3), 161-190 (2002)
7. Schaefer, S., McPhail, T., Warren, J.D.: Image deformation using moving least squares. ACM Trans. Graph. 25(3), 533-540 (2006)
8. Kehagias, A., Petridis, V.: Predictive modular neural networks for time series classification. Neural Networks 10(1), 31-49 (1997)
9. Nanopoulos, A., Alcock, R., Manolopoulos, Y.: Feature-based classification of timeseries data. International Journal of Computer Research 10, 49-61 (2001)
10. Pavlovic, V., Frey, B.J., Huang, T.S.: Time-series classification using mixed-state dynamic bayesian networks. In: CVPR, p. 2609. IEEE Computer Society (1999)
11. Rodríguez, J.J., Alonso, C.J.: Interval and dynamic time warping-based decision trees. In: Proceedings of the 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 548-552. ACM, New York (2004)
12. Rodríguez, J.J., Alonso, C.J., Boström, H.: Learning First Order Logic Time Series Classifiers: Rules and Boosting. In: Zighed, D.A., Komorowski, J., Z? ytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 299-308. Springer, Heidelberg (2000)
13. Kim, S., Smyth, P., Luther, S.: Modeling waveform shapes with random effects segmental hidden markov models. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, UAI 2004, Arlington, Virginia, United States, pp. 309-316. AUAI Press (2004)
14. Mizuhara, Y., Hayashi, A., Suematsu, N.: Embedding of time series data by using dynamic time warping distances. Syst. Comput. Japan 37(3), 1-9 (2006)
15. Xi, X., Keogh, E.J., Shelton, C.R.,Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: ICML. ACM International Conference Proceeding Series, vol. 148, pp. 1033-1040. ACM (2006)
16. Keogh, E.J., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358-386 (2005)
17. Niyogi, P., Girosi, F., Poggio, T.: Incorporating prior information in machine learning by creating virtual examples. Proceedings of the IEEE, 2196-2209 (1998)
18. Loosli, G., Canu, S., Vishwanathan, S.V.N., Smola, A.J.: Invariances in classification: an efficient svm implementation. In: Proceedings of the 11th International Symposium on Applied Stochastic Models and Data Analysis (2005)
19. Loosli, G., Canu, S., Bottou, L.: Training invariant support vector machines using selective sampling. In: Bottou, L., Chapelle, O., DeCoste, D., Weston, J. (eds.) Large Scale Kernel Machines, pp. 301-320. MIT Press, Cambridge (2007)
20. Shimodaira, H.: Noma, K.-i., Nakai,M., Sagayama, S.: Support vector machine with dynamic time-alignment kernel for speech recognition. In: Dalsgaard, P., Lindberg, B., Benner, H., Tan, Z.H. (eds.) INTERSPEECH, ISCA, pp. 1841-1844 (2001)
21. Bahlmann, C., Haasdonk, B., Burkhardt, H.: On-line handwriting recognition with support vector machines - a kernel approach. In: Proc. of the 8th IWFHR, pp. 49-54 (2002)
22. Zhang, D., Zuo, W., Zhang, D., Zhang, H.: Time series classification using support vector machine with gaussian elastic metric kernel. In: ICPR, pp. 29-32. IEEE (2010)
23. Rahimi, A., Recht, B., Darrell, T.: Learning to transform time series with a few examples. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1759-1775 (2007)