Is unlabeled data suitable for multiclass SVM-based web page classification?
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
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The purpose of this paper is to propose a semisupervised learning method for the problem of multiclass classification. We first introduce the Laplacian of a graph and the associated graph kernels which are exploited in many semi-supervised binary classification methods. Then, we will introduce a new multiclass semi-supervised learning method based on a multiclass formulation of SVM. The proposed optimization problems can fully exploit the sparse structure of the Laplacian matrix, which enables us to optimize the problems with a large number of data points by standard optimization algorithms. Some numerical results indicate that our approaches achieve fairly high performance on large scale problems.