Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Support Vector Data Description
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Linear model combining by optimizing the Area under the ROC curve
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
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Kernel-based one-class classification is a special type of classification problem, and is widely used as the outlier detection and novelty detection technique. One of the most commonly used method is the support vector dada description (SVDD). However, the performance is mostly affected by which kernel is used. A promising way is to learn the kernel from the data automatically. In this paper, we focus on the problem of choosing the optimal kernel from a kernel convex hull for the given one-class classification task, and propose a new approach. Kernel methods work by nonlinearly mapping the data into an embedding feature space, and then searching the relations among this space, however this mapping is implicitly performed by the kernel function. How to choose a suitable kernel is a difficult problem. In our method, we first transform the data points linearly so that we obtain a new set whose variances equal unity. Then we choose the minimum embedding ball as the criterion to learn the optimal kernel matrix over the kernel convex hull. It leads to the convex quadratically constrained quadratic programming (QCQP). Experiments results on a collection of benchmark data sets demonstrated the effectiveness of the proposed method.