NDLI: MEGA---the maximizing expected generalization algorithm for learning complex query concepts

Content Provider	ACM Digital Library
Author	Chang, Edward Li, Beitao
Copyright Year	2003
Abstract	Specifying exact query concepts has become increasingly challenging to end-users. This is because many query concepts (e.g., those for looking up a multimedia object) can be hard to articulate, and articulation can be subjective. In this study, we propose a query-concept learner that learns query criteria through an intelligent sampling process. Our concept learner aims to fulfill two primary design objectives: (1) it has to be expressive in order to model most practical query concepts and (2) it must learn a concept quickly and with a small number of labeled data since online users tend to be too impatient to provide much feedback. To fulfill the first goal, we model query concepts in $\textit{k}-CNF,$ which can express almost all practical query concepts. To fulfill the second design goal, we propose our maximizing expected generalization algorithm (MEGA), which converges to target concepts quickly by its two complementary steps: sample selection and concept refinement. We also propose a divide-and-conquer method that divides the concept-learning task into $\textit{G}$ subtasks to achieve speedup. We notice that a task must be divided carefully, or search accuracy may suffer. Through analysis and mining results, we observe that organizing image features in a multiresolution manner, and minimizing intragroup feature correlation, can speed up query-concept learning substantially while maintaining high search accuracy. Through examples, analysis, experiments, and a prototype implementation, we show that MEGA converges to query concepts significantly faster than traditional methods.
Starting Page	347
Ending Page	382
Page Count	36
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/944012.944014
Volume Number	21
Issue Number	4
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-10-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Active learning Data mining Query concept Relevance feedback
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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