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Published in:
2014 | OriginalPaper | Chapter
Learning Anisotropic RBF Kernels
Authors : Fabio Aiolli, Michele Donini
Published in: Artificial Neural Networks and Machine Learning – ICANN 2014
Publisher: Springer International Publishing
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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.