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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4741
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dc.contributor.authorTiwari, Arunaen_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:35:20Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:35:20Z-
dc.date.issued2014-
dc.identifier.citationChandel, A. S., Tiwari, A., & Patel, O. P. (2014). Improved constructive semi-supervised classification algorithm for handling the overlapping of hyperspheres. Paper presented at the Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014, 130-133. doi:10.1109/ICICICT.2014.6781265en_US
dc.identifier.otherEID(2-s2.0-84899049176)-
dc.identifier.urihttps://doi.org/10.1109/ICICICT.2014.6781265-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/4741-
dc.description.abstractAn Improved Constructive Semi-Supervised Classification Algorithm (ICS-SCA) is proposed to handle the problem of overlapping among hyperspheres of different classes. The overlapping problem occurs in Constructive Semi-Supervised Classification Algorithm proposed in [1]. Unlike CS-SCA[1], approach proposed here handles the samples lying in overlapping region of hyperspheres belonging to the different classes while it allows the overlapping among hyperspheres belonging to the same classes. Proposed approach is tested with the datasets that were taken in CS-SCA[1] and improved results have shown in table. © 2014 IEEE.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.sourceProceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2014en_US
dc.subjectAlgorithmsen_US
dc.subjectIntelligent computingen_US
dc.subjectBinary neural networksen_US
dc.subjectDifferent classen_US
dc.subjectHyper-spheresen_US
dc.subjectOverlappingen_US
dc.subjectOverlapping regionsen_US
dc.subjectSemi-supervised classificationen_US
dc.subjectSupervised learningen_US
dc.titleImproved constructive semi-supervised classification algorithm for handling the overlapping of hyperspheresen_US
dc.typeConference Paperen_US
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