| p-Margin Kernel Learning Machine with Magnetic Field Effect for Both Binary Classification andNovelty Detectio |
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| Jianwen Tao,Shitong Wang,Wenjun Hu,Wenhao Ying. p-Margin Kernel Learning Machine with Magnetic Field Effect for Both Binary Classification andNovelty Detectio. International Journal of Software and Informatics, 2010,4(3):305~324 |
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| Fund:This work was supported by Natural Science Foundation of China under Grant Nos. 60975027 and60903100, Natural Science Foundation of Ningbo City under Grant No.2009A610080. |
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| Abstract:A novel p-margin kernel learning machine (p-MKLM) with magnetic field effect
is proposed in allusion to pattern classification problem in this paper. In the Mercer induced
feature space, p-MKLM can effectively resolve one-class/binary classification problems. By
introducing an adjustable magnetic field density q, the basic idea of p-MKLM is to find an
optimal hyperplane with magnetic field effect such that the distance between one class and
the hyperplane is as small as possible due to the magnetic attractive effect, while at the
same time the margin between the hyperplane and the other class is as large as possible
due to magnetic repulsion, thus implementing both maximum between-class margin and
minimum within-class volume so as to improve the generalization capability of the proposed
method. To construct such a hyperplane with magnetic field effect, it is only needed to solve
a convex quadratic programming problem which can be effciently solved with the off-the-
shelf software packages for training learning machine. Experimental results obtained with
benchmarking datasets show that the proposed algorithm is effective and competitive to
other related methods in such cases as two-class and one-class (or novelty detection) pattern
classification respectively. |
| keywords:magnetic field effect pattern classification novelty detection support vectormachine kernel approach |
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