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• W2114519103 abstract "Learning is considered as a dynamic process described by a trajectory on a statistical manifold, and a topology is introduced defining trajectories continuous in information. The analysis generalises the application of Orlicz spaces in non-parametric information geometry to topological function spaces with asymmetric gauge functions (e.g. quasi-metric spaces defined in terms of KL divergence). Optimality conditions are formulated for dynamical constraints, and two main results are outlined: 1) Parametrisation of optimal learning trajectories from empirical constraints using generalised characteristic potentials; 2) A gradient theorem for the potentials defining optimal utility and information bounds of a learning system. These results not only generalise some known relations of statistical mechanics and variational methods in information theory, but also can be used for optimisation of the exploration-exploitation balance in online learning systems." @default.
• W2114519103 created "2016-06-24" @default.
• W2114519103 creator A5006396647 @default.
• W2114519103 date "2009-03-01" @default.
• W2114519103 modified "2023-09-27" @default.
• W2114519103 title "Bounds of optimal learning" @default.
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• W2114519103 doi "https://doi.org/10.1109/adprl.2009.4927545" @default.
• W2114519103 hasPublicationYear "2009" @default.
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