Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Learning for Regression and Classification by Inverse Distance
  Weighting   This paper proposes an active learning algorithm for solving regression and
classification problems based on inverse-distance weighting functions for
selecting the feature vectors to query. The algorithm has the following
features: (i) supports both pool-based and population-based sampling; (ii) is
independent of the type of predictor used; (iii) can handle known and unknown
constraints on the queryable feature vectors; and (iv) can run either
sequentially, or in batch mode, depending on how often the predictor is
retrained. The method's potential is shown in numerical tests on illustrative
synthetic problems and real-world regression and classification datasets from
the UCI repository. A Python implementation of the algorithm that we call IDEAL
(Inverse-Distance based Exploration for Active Learning), is available at
\url{http://cse.lab.imtlucca.it/~bemporad/ideal}.

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The categories of this paper are: hep-lat
19
The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
2
The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg