Abstract
With the recent development of network and sensor technologies, vast amounts of data are being continuously generated in real time from real-world environments. Such data includes in many noise, and it is not easy to predict that distribution underlying the data in advance. Probability density estimation is a critical task of machine learning, but it is difficult to accomplish it for big data in the real world. For handling such data, we propose a robust fast online multivariate non-parametric density estimator. Our proposed method extends the kernel density estimation and Self-Organizing Incremental Neural Network. The experimental results show that our proposed method outperforms or achieves a state-of-the-art performance.
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Nakamura, Y., Hasegawa, O. (2013). Robust Fast Online Multivariate Non-parametric Density Estimator. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42042-9_23
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DOI: https://doi.org/10.1007/978-3-642-42042-9_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42041-2
Online ISBN: 978-3-642-42042-9
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