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Asymptotic Analysis of Objectives based on Fisher Information in Active Learning
In particular, we show that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio.
Hierarchical Fisher Kernels for Longitudinal Data
We develop new techniques for time series classification based on hierarchical Bayesian generative models (called mixed-effect models) and the Fisher kernel derived from them.
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