Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Editorial: Special issue on mining low-quality data
Knowledge and Information Systems - Special Issue on Mining Low-Quality Data
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Sample Selection Bias Correction Theory
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Cross domain distribution adaptation via kernel mapping
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Large margin transductive transfer learning
Proceedings of the 18th ACM conference on Information and knowledge management
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When training and testing data are drawn from different distributions, the performance of the classification model will be low. Such a problem usually comes from sample selection bias or transfer learning scenarios. In this paper, we propose a novel multiple kernel learning framework improved by Maximum Mean Discrepancy (MMD) to solve the problem. This new model not only utilizes the capacity of kernel learning to construct a nonlinear hyperplane which maximizes the separation margin, but also reduces the distribution discrepancy between training and testing data simultaneously, which is measured by MMD. This approach is formulated as a bi-objective optimization problem. Then an efficient optimization algorithm based on gradient descent and quadratic programming [13] is adopted to solve it. Extensive experiments on UCI and text datasets show that the proposed model outperforms traditional multiple kernel learning model in sample selection bias and transfer learning scenarios.