Pattern Synthesis with Active Learning Using Classifier Combination.
Date of Submission
December 2005
Date of Award
Winter 12-12-2006
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Machine Intelligence Unit (MIU-Kolkata)
Supervisor
Ghosh, Ashish (MIU-Kolkata; ISI)
Abstract (Summary of the Work)
In this dissertation, the problem of Pattern Synthesis in conjunction with Ac- tive Learning has been considered. Synthesising new patterns though initially appears to be hardly possible, the research community has been dealing with the inverse problem i.e., the data condensation problem fairly well. Based on this idea a novel pattern synthesis technique mostly mimicing the DBSCAN clustering algorithm has been proposed. The patterns crrated in this way have been used to the Active Learning process. Moreover, it is well known that in many situations a Classifier Combination, consistently outperforms a single best classifier, and when there are a number of classifiers of different nature, one can use the Query By Committee(QBC) approach for actively selecting the new patterns for further training. In this dissertation Active Learning using Classifier Combination coupled with Pattern Synthesis has been studied. Experimental evaluation also reveals that this is slightly better than the naive Passive Learning approach using Classifier Combination. In this dissertation, the problem of Pattern Synthesis in conjunction
Control Number
ISI-DISS-2005-146
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
DOI
http://dspace.isical.ac.in:8080/jspui/handle/10263/6315
Recommended Citation
S., Lokesh Kumar, "Pattern Synthesis with Active Learning Using Classifier Combination." (2006). Master’s Dissertations. 107.
https://digitalcommons.isical.ac.in/masters-dissertations/107
Comments
ProQuest Collection ID: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqm&rft_dat=xri:pqdiss:28843123