Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Asymptotic Analysis of Objectives based on Fisher Information in Active
  Learning   Obtaining labels can be costly and time-consuming. Active learning allows a
learning algorithm to intelligently query samples to be labeled for efficient
learning. Fisher information ratio (FIR) has been used as an objective for
selecting queries in active learning. However, little is known about the theory
behind the use of FIR for active learning. There is a gap between the
underlying theory and the motivation of its usage in practice. In this paper,
we attempt to fill this gap and provide a rigorous framework for analyzing
existing FIR-based active learning methods. In particular, we show that FIR can
be asymptotically viewed as an upper bound of the expected variance of the
log-likelihood ratio. Additionally, our analysis suggests a unifying framework
that not only enables us to make theoretical comparisons among the existing
querying methods based on FIR, but also allows us to give insight into the
development of new active learning approaches based on this objective.

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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn