Abstract
We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets demonstrate that our method generally outperforms state-of-the-art distance metric learning methods, including the Large Margin Nearest Neighbor Classification family of methods.
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Aiolli, F., Donini, M. (2014). Learning Anisotropic RBF Kernels. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_65
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DOI: https://doi.org/10.1007/978-3-319-11179-7_65
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11178-0
Online ISBN: 978-3-319-11179-7
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