Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/7099
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRai, P.-
dc.contributor.authorPrabhumoye, S.-
dc.contributor.authorKhattri, P.-
dc.contributor.authorSandhu, L.R.S.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T09:58:30Z-
dc.date.available2020-03-30T09:58:30Z-
dc.date.issued2014-
dc.identifier.citationSmart Innovation, Systems and Technologies, 2014, Vol.27, VOL 1, pp.67-76en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/7099-
dc.description.abstractLearning to Rank is a concept that focuses on the application of supervised or semi-supervised machine learning techniques to develop a ranking model based on training data. In this paper, we present a learning based search engine that uses supervised machine learning techniques like selection based and review based algorithms to construct a ranking model. Information retrieval techniques are used to retrieve the relevant URLs by crawling the Web in a Breadth-First manner, which are then used as training data for the supervised and review based machine learning techniques to train the crawler. We used the Gradient Descent Algorithm to compare the two techniques and for result analysis. � Springer International Publishing Switzerland 2014.en_US
dc.titleA prototype of an intelligent search engine using machine learning based training for learning to ranken_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.