Liliann Miche

• MSc from ENSIMAG, Grenoble INPG, France (SoC designs for telecom applications)
• DSc (Machine Learning for Security applications) from Aalto University (Finland) and INPG (France)

(Full list at ‪Liliann Miche‬ - ‪Google Scholar‬)

• Miche, Y., Oliver, I., Ren, W., Holtmanns, S., Akusok, A., & Lendasse, A. (2017). Practical estimation of mutual information on non-Euclidean spaces (pp. 123–136). Presented at the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer, Cham.
• Miche, Y., Oliver, I., Holtmanns, S., Kalliola, A., Akusok, A., Lendasse, A., & Björk, K.-M. (2016). Data anonymization as a vector quantization problem: Control over privacy for health data (pp. 193–203). Presented at the International Conference on Availability, Reliability, and Security, Springer, Cham.
• Miche, Y., Ren, W., Oliver, I., Holtmanns, S., & Lendasse, A. (2019). A framework for privacy quantification: measuring the impact of privacy techniques through mutual information, distance mapping, and machine learning. Cognitive Computation, 11(2), 241–261.
• Roshan, S., Miche, Y., Akusok, A., & Lendasse, A. (2018). Adaptive and online network intrusion detection system using clustering and extreme learning machines. Journal of the Franklin Institute, 355(4), 1752–1779.
• Laaki, H., Miche, Y., & Tammi, K. (2019). Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery. IEEE Access, 7, 20325–20336.
• Akusok, A., Björk, K.-M., Miche, Y., & Lendasse, A. (2015). High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access, 3, 1011–1025.
• Akusok, A., Miche, Y., Björk, K.-M., & Lendasse, A. (2019). Per-sample prediction intervals for extreme learning machines. International Journal of Machine Learning and Cybernetics, 10(5), 991–1001.
• Atli, B. G., Miche, Y., Kalliola, A., Oliver, I., Holtmanns, S., & Lendasse, A. (2018). Anomaly-based intrusion detection using extreme learning machine and aggregation of network traffic statistics in probability space. Cognitive Computation, 10(5), 848–863.
• Cambria, E., Huang, G.-B., Kasun, L. L. C., Zhou, H., Vong, C. M., Lin, J., et al. (2013). Extreme learning machines [trends & controversies]. IEEE intelligent systems, 28(6), 30–59.
• De Souza Junior, A. H., Corona, F., Barreto, G. A., Miche, Y., & Lendasse, A. (2015). Minimal learning machine: a novel supervised distance-based approach for regression and classification. Neurocomputing, 164, 34–44.
• Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., & Lendasse, A. (2009). OP-ELM: optimally pruned extreme learning machine. IEEE transactions on neural networks, 21(1), 158–162.
• Miche, Y., Van Heeswijk, M., Bas, P., Simula, O., & Lendasse, A. (2011). TROP-ELM: a double-regularized ELM using LARS and Tikhonov regularization. Neurocomputing, 74(16), 2413–2421.
• Ren, W., Miche, Y., Oliver, I., Holtmanns, S., Bjork, K.-M., & Lendasse, A. (2017). On Distance Mapping from non-Euclidean Spaces to Euclidean Spaces (pp. 3–13). Presented at the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Springer.

Full list at Liliann Miche - Google Patents