Maximum margin based semi-supervised spectral kernel learning
Refereed conference paper presented and published in conference proceedings


摘要Semi-supervised kernel learning is attracting increasing research interests recently. It works by learning an embedding of data from the input space to a Hilbert space using both labeled data and unlabeled data, and then searching for relations among the embedded data points. One of the most well-known semi-supervised kernel learning approaches is the spectral kernel learning methodology which usually tunes the spectral empirically or through optimizing some generalized performance measures. However, the kernel designing process does not involve the bias of a kernel-based learning algorithm, the deduced kernel matrix cannot necessarily facilitate a specific learning algorithm. To supplement the spectral kernel learning methods, this paper proposes a novel approach, which not only learns a kernel matrix by maximizing another generalized performance measure, the margin between two classes of data, but also leads directly to a convex optimization method for learning the margin parameters in support vector machines. Moreover, experimental results demonstrate that our proposed spectral kernel learning method achieves promising results against other spectral kernel learning methods. ©2007 IEEE.
著者Xu Z., Zhu J., Lyu M.R., King I.
會議名稱2007 International Joint Conference on Neural Networks, IJCNN 2007
會議地點Orlando, FL
詳細描述(International Joint Conference on Neural Networks)
頁次418 - 423

上次更新時間 2020-23-10 於 02:23