Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating Photometric Redshifts of Quasars via K-nearest Neighbor
  Approach Based on Large Survey Databases   We apply one of lazy learning methods named k-nearest neighbor algorithm
(kNN) to estimate the photometric redshifts of quasars, based on various
datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky
Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS
sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE
sample). The influence of the k value and different input patterns on the
performance of kNN is discussed. kNN arrives at the best performance when k is
different with a special input pattern for a special dataset. The best result
belongs to the SDSS-UKIDSS-WISE sample. The experimental results show that
generally the more information from more bands, the better performance of
photometric redshift estimation with kNN. The results also demonstrate that kNN
using multiband data can effectively solve the catastrophic failure of
photometric redshift estimation, which is met by many machine learning methods.
By comparing the performance of various methods for photometric redshift
estimation of quasars, kNN based on KD-Tree shows its superiority with the best
accuracy for our case.

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The categories of this paper are: eess
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The categories of this paper are: cmp-lg
2
The categories of this paper are: math
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The categories of this paper are: econ
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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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: stat
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The categories of this paper are: adap-org
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The categories of this paper are: cond-mat