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» A theory of learning with similarity functions
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CORR
2010
Springer
103views Education» more  CORR 2010»
14 years 9 months ago
Asymptotic Learning Curve and Renormalizable Condition in Statistical Learning Theory
Bayes statistics and statistical physics have the common mathematical structure, where the log likelihood function corresponds to the random Hamiltonian. Recently, it was discovere...
Sumio Watanabe
LICS
2005
IEEE
15 years 3 months ago
Inverse and Implicit Functions in Domain Theory
We construct a domain-theoretic calculus for Lipschitz and differentiable functions, which includes addition, subtraction and composition. We then develop a domaintheoretic versio...
Abbas Edalat, Dirk Pattinson
88
Voted
MMS
2010
14 years 8 months ago
Asynchronous reflections: theory and practice in the design of multimedia mirror systems
-- In this paper, we present a theoretical framing of the functions of a mirror by breaking the synchrony between the state of a reference object and its reflection. This framing p...
Wei Zhang, Bo Begole, Maurice Chu
JMLR
2008
95views more  JMLR 2008»
14 years 9 months ago
Learning Similarity with Operator-valued Large-margin Classifiers
A method is introduced to learn and represent similarity with linear operators in kernel induced Hilbert spaces. Transferring error bounds for vector valued large-margin classifie...
Andreas Maurer
ICML
2009
IEEE
15 years 10 months ago
Learning kernels from indefinite similarities
Similarity measures in many real applications generate indefinite similarity matrices. In this paper, we consider the problem of classification based on such indefinite similariti...
Yihua Chen, Maya R. Gupta, Benjamin Recht