International Journal of Computer Vision
Machine Learning
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
A fast dual method for HIK SVM learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Object templates for visual place categorization
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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A structural similarity kernel is presented in this paper for SVM learning, especially for learning with imbalanced datasets. Kernels in SVM are usually pairwise, comparing the similarity of two examples only using their feature vectors. By building a neighborhood graph (kNN graph) using the training examples, we propose to utilize the similarity of linking structures of two nodes as an additional similarity measure. The structural similarity measure is proven to form a positive definite kernel and is shown to be equivalent to a regularization term that encourages balanced weights in all local neighborhoods. Analogous to the unsupervised HITS algorithm, the structural similarity kernel turns hub scores into signed authority scores, and is particularly effective in dealing with imbalanced learning problems. Experimental results on several benchmark datasets show that structural similarity can help the linear and the histogram intersection kernel to match or surpass the performance of the RBF kernel in SVM learning, and can significantly improve imbalanced learning results.