Learning Kernels from Distance Constraints
نویسندگان
چکیده
منابع مشابه
Distance Metric Learning with Kernels
In this paper, we propose a feature weighting method that works in both the input space and the kernel-induced feature space. It assumes only the availability of similarity (dissimilarity) information, and the number of parameters in the transformation does not depend on the number of features. Besides feature weighting, it can also be regarded as performing nonparametric kernel adaptation. Exp...
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Bessière et al. (AAAI’08) showed that several intractable global constraints can be efficiently propagated when certain natural problem parameters are small. In particular, the complete propagation of a global constraint is fixed-parameter tractable in k – the number of holes in domains – whenever bound consistency can be enforced in polynomial time; this applies to the global constraints AtMos...
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ژورنال
عنوان ژورنال: IPSJ Digital Courier
سال: 2006
ISSN: 1349-7456
DOI: 10.2197/ipsjdc.2.441