Abstract
Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theoretically guaranteed support vector machine training algorithms, and (II) to develop systematic and efficient methods for finding “outliers”, i.e., examples having an inherent error.
This work was started when the first and third authors visited Centre de Recerca Mathemática, Spain.
Supported in part by EU ESPRIT IST-1999-14186 (ALCOM-FT), EU EP27150 (Neurocolt II), Spanish Government PB98-0937-C04 (FRESCO), and CIRIT 1997SGR-00366.
Supported in part by a Grant-in-Aid (C-13650444) from the Ministry of Education, Science, Sports and Culture of Japan.
Supported in part by a Grant-in-Aid for Scientific Research on Priority Areas “Discovery Science” from the Ministry of Education, Science, Sports and Culture of Japan.
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Balcázar, J., Dai, Y., Watanabe, O. (2001). A Random Sampling Technique for Training Support Vector Machines. In: Abe, N., Khardon, R., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2001. Lecture Notes in Computer Science(), vol 2225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45583-3_11
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DOI: https://doi.org/10.1007/3-540-45583-3_11
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