Abstract
Support vector machines (SVMs) are gaining much popularity as effective methods in machine learning. In pattern classification problems with two class sets, their basic idea is to find a maximal margin separating hyperplane which gives the greatest separation between the classes in a high dimensional feature space. However, the idea of maximal margin separation is not quite new: in 1960’s the multi-surface method (MSM) was suggested by Mangasarian. In 1980’s, linear classifiers using goal programming were developed extensively. This paper considers SVMs from a viewpoint of goal programming, and proposes a new method based on the total margin instead of the shortest distance between learning data and separating hyperplane.
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© 2003 Springer-Verlag Berlin Heidelberg
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Nakayama, H., Yun, Y., Asada, T., Yoon, M. (2003). Goal Programming Approaches to Support Vector Machines. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_50
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DOI: https://doi.org/10.1007/978-3-540-45224-9_50
Publisher Name: Springer, Berlin, Heidelberg
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