Hierarchical and Reweighting Cluster Kernels for Semi-Supervised Learning

  • Zalán Bodó Department of Mathematics and Computer Science Babes–Bolyai University Kogalniceanu 1, 400084 Cluj-Napoca, Romania
  • Lehel Csató Department of Mathematics and Computer Science Babes–Bolyai University Kogalniceanu 1, 400084 Cluj-Napoca, Romania

Abstract

Recently semi-supervised methods gained increasing attention and many novel semi-supervised learning algorithms have been proposed. These methods exploit the information contained in the usually large unlabeled data set in order to improve classification or generalization performance. Using data-dependent kernels for kernel machines one can build semi-supervised classifiers by building the kernel in such a way that feature space dot products incorporate the structure of the data set. In this paper we propose two such methods: one using specific hierarchical clustering, and another kernel for reweighting an arbitrary base kernel taking into account the cluster structure of the data.

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Published
2010-11-01
How to Cite
BODÓ, Zalán; CSATÓ, Lehel. Hierarchical and Reweighting Cluster Kernels for Semi-Supervised Learning. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 5, n. 4, p. 469-476, nov. 2010. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2496>. Date accessed: 06 aug. 2020. doi: https://doi.org/10.15837/ijccc.2010.4.2496.

Keywords

Kernel methods, semi-supervised learning, clustering