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Maximal Discrepancy for Support Vector Machines., , and . Neurocomputing, 74 (9): 1436-1443 (2011)In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine., , , and . Neural Processing Letters, 36 (3): 275-283 (2012)A FPGA Core Generator for Embedded Classification Systems., , , and . Journal of Circuits, Systems, and Computers, 20 (2): 263-282 (2011)Smartphone battery saving by bit-based hypothesis spaces and local Rademacher Complexities., , , and . IJCNN, page 3916-3921. IEEE, (2014)Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory., , , , and . IJCNN, page 1-8. IEEE, (2010)Feed-Forward Support Vector Machine Without Multipliers., , , and . IEEE Trans. Neural Networks, 17 (5): 1328-1331 (2006)Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic., , , , and . J. Univers. Comput. Sci., 19 (9): 1295-1314 (2013)Data-Driven Photovoltaic Power Production Nowcasting and Forecasting for Polygeneration Microgrids., , , , and . IEEE Syst. J., 12 (3): 2842-2853 (2018)Nested Sequential Minimal Optimization for Support Vector Machines., , , , and . ICANN (2), volume 7553 of Lecture Notes in Computer Science, page 156-163. Springer, (2012)A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks., , , , , and . Int. J. Data Sci. Anal., 9 (1): 95-111 (2020)