| References: [1] Aggarwal, C. C., Ashish, N., Sheth, A. P. (2013). The internet of things: A survey from the data-centric perspective. In: Managing and Mining Sensor Data. Springer.
[2] Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B., Braun, M. Moa: Massive online analysis.
[3] Jaka Demšar. (2012). Explanation of predictive models and individual predictions in incremental learning (In Slovene). B.S. Thesis, University of Ljubljana.
[4] Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press, 1st edition.
[5] Gama, J. (2010). Knowledge Discovery from Data Streams. Chapman & Hall/CRC, 1st edition.
[6] Haussler, D. (1995). Overview of the probably approximately correct (PAC) learning framework.
[7] Havre, S., Hetzler, B., Nowell, L. (2000). Themeriver: Visualizing theme changes over time. In: Proc. IEEE Symposium on Information Visualization.
[8] Ratanamahatana, C., Lin 0001, J., Gunopulos, D., Keogh, E. J., Vlachos, M., Das, G. (2005). Mining time series data. In: The Data Mining and Knowledge Discovery Handbook. Springer.
[9] Sebastião, R., Gama, J. (2009). A study on change detection methods. In: Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence, EPIA.
[10] Page, E. S. (1954). Continuous Inspection Schemes. Biometrika, 41, 100-115.
[11] Street, W. N., Kim, Y. S. (2001). A streaming ensemble algorithm for large-scale classification. In: Proceedings of the 7th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’01, New York, NY, USA.
[12] Strumbelj, E., Kononenko, I. (2010). An efficient explanation of individual classifications using game theory. The Journal of Machine Learning Research, 11, 1–18.
[13] Strumbelj, E., Kononenko, I., Robnik Sikonja, M. (2009). Explaining instance classifications with interactions of subsets of feature values. Data & Knowledge Engineering, 68(10) 886–904, (October). |
