Abstract
In this paper, we introduce machine learning algorithms of time-series data based on Self-organizing Incremental Neural Network (SOINN). SOINN is known as a powerful tool for incremental unsupervised clustering. Using a similarity threshold based and a local error-based insertion criterion, it is able to grow incrementally and to accommodate input patterns of on-line non-stationary data distribution. These advantages of SOINN are available for modeling of time-series data. Firstly, we explain an on-line supervised learning approach, SOINN-DTW, for recognition of time-series data that are based on Dynamic Time Warping (DTW). Second, we explain an incremental clustering approach, Hidden-Markov-Model Based SOINN (HBSOINN), for time-series data. This paper summarizes SOINN based time-series modeling approaches (SOINN-DTW, HBSOINN) and the advantage of SOINN-based time-series modeling approaches compared to traditional approaches such as HMM.
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References
Shen, F., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Networks 19(1), 90–106 (2006)
Okada, S., Hasegawa, O.: Classification of temporal data based on selforganizing incremental neural network. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4669, pp. 465–475. Springer, Heidelberg (2007)
Okada, S., Hasegawa, O.: On-line learning of sequence data based on self-organizing incremental neural network. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008 (IEEE World Congress on Computational Intelligence), pp. 3847–3854 (2008)
Okada, S., Nishida, T.: Incremental clustering of gesture patterns based on a self organizing incremental neural network. In: IEEE International Joint Conference on Neural Networks (IJCNN 2009) (2009)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. Proc. IEEE, 257–286 (1989)
Wilson, A., Bobick, A.: Learning visual behavior for gesture analysis. In: Proc. IEEE International Symposium on Computer Vision, vol. 5A Motion2 (1995)
Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Transactions on Speech and Audio Processing 2(2), 291–298 (1994)
Leggetter, C., Woodland, P.: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Computer Speech and Language 9(2), 171–185 (1995)
Mongillo, G., Deneve, S.: Online learning with hidden markov models. Neural computation 20(7), 1706–1716 (2008)
Alon, J., Sclaroff, S., Kollios, G., Pavlovic, V.: Discovering clusters in motion time-series data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 375–381 (2003)
Yin, J., Yang, Q.: Integrating hidden markov models and spectral analysis for sensory time series clustering. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 506–513 (2005)
Kulić, D., Takano, W., Nakamura, Y.: Incremental learning, clustering and hierarchy formation of whole body motion patterns using adaptive hidden markov chains. Int. J. Rob. Res. 27(7), 761–784 (2008)
Ghahramani, Z., Jordan, M.I., Smyth, P.: Factorial hidden markov models. In: Machine Learning. MIT Press, Cambridge (1997)
Rabiner, L., Juang, B.: Fundamentals of Speech Recognition. PTR Prentice-Hall, Inc., Englewood Cliffs (1993)
Nakagawa, S.: Speaker-independent consonant recognition in continuous speech by a stochastic dynamic time warping method. In: Proc. International Conference Pattern Recognition., vol. 70, pp. 925–928 (1986)
Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley Sons, Inc., Canada (2001)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2007)
Okada, S., Ishibashi, S., Nishida, T.: On-line unsupervised segmentation for multidimensional time-series data and application to spatiotemporal gesture data. In: IEA/AIE 2010 (2010)
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Okada, S., Hasegawa, O., Nishida, T. (2010). Machine Learning Approaches for Time-Series Data Based on Self-Organizing Incremental Neural Network. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_75
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DOI: https://doi.org/10.1007/978-3-642-15825-4_75
Publisher Name: Springer, Berlin, Heidelberg
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