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Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression.

, , and . IEEE Trans. Smart Grid, 4 (1): 549-556 (2013)

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PAC-bayesian analysis of distribution dependent priors: Tighter risk bounds and stability analysis., , and . Pattern Recognit. Lett., (2016)A FPGA Core Generator for Embedded Classification Systems., , , and . Journal of Circuits, Systems, and Computers, 20 (2): 263-282 (2011)Maximal Discrepancy for Support Vector Machines., , and . Neurocomputing, 74 (9): 1436-1443 (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)In-sample Model Selection for Trimmed Hinge Loss Support Vector Machine., , , and . Neural Processing Letters, 36 (3): 275-283 (2012)Feed-Forward Support Vector Machine Without Multipliers., , , and . IEEE Trans. Neural Networks, 17 (5): 1328-1331 (2006)Nested Sequential Minimal Optimization for Support Vector Machines., , , , and . ICANN (2), volume 7553 of Lecture Notes in Computer Science, page 156-163. Springer, (2012)Bridging Cognitive Models and Recommender Systems., , , , and . Cogn. Comput., 12 (2): 426-427 (2020)K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines., , , and . DMIN, page 291-297. CSREA Press, (2009)