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Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap.

, , and . Neural Processing Letters, 11 (1): 51-58 (2000)

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Possibility and Necessity Pattern Classification using an Interval Arithmetic Perceptron., and . Neural Computing and Applications, 8 (1): 40-52 (1999)Support Vector Machines: A Comparison of Some Kernel Functions., , and . IIA/SOCO, ICSC Academic Press, Rochester, NY, USA, (1999)Learning with few bits on small-scale devices: From regularization to energy efficiency., , , and . ESANN, (2014)The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers., , , and . NIPS, page 585-593. (2011)Solving linear equation systems on vector computers with maximum efficiency., , , , and . Parallel Computing, 8 (1-3): 133-139 (1988)Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency., , , and . Neurocomputing, (2015)Using K-Winner Machines for domain analysis., and . Neurocomputing, (2004)Digital implementation of hierarchical vector quantization., , and . IEEE Trans. Neural Networks, 14 (5): 1072-1084 (2003)Statistically controlled activation weight initialization (SCAWI)., and . IEEE Trans. Neural Networks, 3 (4): 627-631 (1992)K-winner machines for pattern classification., , and . IEEE Trans. Neural Networks, 12 (2): 371-385 (2001)