  The application of machine learning principles in the photometric search of
elusive astronomical objects has been a less-explored frontier of research.
Here we have used three methods: the Neural Network and two variants of
k-Nearest Neighbour, to identify brown dwarf candidates using the photometric
colours of known brown dwarfs. We initially check the efficiencies of these
three classification techniques, both individually and collectively, on known
objects. This is followed by their application to three regions in the sky,
namely Hercules (2 deg x 2 deg), Serpens (9 deg x 4 deg) and Lyra (2 deg x 2
deg). Testing these algorithms on sets of objects that include known brown
dwarfs shows a high level of completeness. This includes the Hercules and
Serpens regions where brown dwarfs have been detected. We use these methods to
search and identify brown dwarf candidates towards the Lyra region. We infer
that the collective method of classification, also known as ensemble
classifier, is highly efficient in the identification of brown dwarf
candidates.
