Learning with Non-Positive Kernels
In this paper we show that many kernel methods can be adapted to deal with indefinite kernels, that is, kernels which are not positive semidefinite. They do not satisfy Mercer's condition and they induce associated functional spaces called Reproducing Kernel Kreǐn Spaces (RKKS), a generalization of Reproducing Kernel Hubert Spaces (RKHS). Machine learning in RKKS shares many "nice" properties of learning in RKHS, such as orthogonality and projection. However, since the kernels are indefinite,...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of 21st International Conference on Machine Learning (ICML-2004)|
|01_Ong_Learning_with_Non-Positive_2004.pdf||1.46 MB||Adobe PDF||Request a copy|
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