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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)Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic., , , , and . J. Univers. Comput. Sci., 19 (9): 1295-1314 (2013)Nested Sequential Minimal Optimization for Support Vector Machines., , , , and . ICANN (2), volume 7553 of Lecture Notes in Computer Science, page 156-163. Springer, (2012)In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models., , , , , , and . ESANN, (2021)The 'K' in K-fold Cross Validation., , , , and . ESANN, (2012)Dropout Prediction at University of Genoa: a Privacy Preserving Data Driven Approach., , , and . ESANN, (2017)Improving the Union Bound: a Distribution Dependent Approach., , and . ESANN, page 423-428. (2020)Societal Issues in Machine Learning: When Learning from Data is Not Enough., , , , , and . ESANN, (2019)Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification., , , , and . NeurIPS, page 12739-12750. (2019)