86 research outputs found
A Learning Algorithm based on High School Teaching Wisdom
A learning algorithm based on primary school teaching and learning is
presented. The methodology is to continuously evaluate a student and to give
them training on the examples for which they repeatedly fail, until, they can
correctly answer all types of questions. This incremental learning procedure
produces better learning curves by demanding the student to optimally dedicate
their learning time on the failed examples. When used in machine learning, the
algorithm is found to train a machine on a data with maximum variance in the
feature space so that the generalization ability of the network improves. The
algorithm has interesting applications in data mining, model evaluations and
rare objects discovery
Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey
We present a catalogue of about 6 million unresolved photometric detections
in the Sloan Digital Sky Survey Seventh Data Release classifying them into
stars, galaxies and quasars. We use a machine learning classifier trained on a
subset of spectroscopically confirmed objects from 14th to 22nd magnitude in
the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036
stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS
{\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed
quasars and 99.51% of stars to i 21.3 in the colour window that we study.
The level of contamination due to data artefacts for objects beyond is
highly uncertain and all mention of completeness and contamination in the paper
are valid only for objects brighter than this magnitude. However, a comparison
of the predicted number of quasars with the theoretical number counts shows
reasonable agreement.Comment: 16 pages, Ref. No. MN-10-2382-MJ.R2, accepted for publication in
MNRAS Main Journal, April 201
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