Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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The high pace rise in online as well as offline multimedia un annotated data and associated mining applications have demanded certain efficient mining algorithm. Multiple instance learning (MIL) has emerged as one of the most effective solutions for huge un annotated data mining. Still, it requires enhancement in instance selection to enable optimal mining and classification of huge multimedia data. Considering critical multimedia mining applications, such as medical data processing or content based information retrieval, the instance verification can be of great significance to optimize MIL. With this motivation, in this paper, Multi-Instance, Multi-Cluster based MIL scheme (MIMC-MIL) has been proposed to perform efficient multimedia data mining and classification with huge un annotated data with different features. The proposed system employs soft max approximation techniques with a novel loss factor and inter-instance distance based weight estimation scheme for instance probability substantiation in bags.
Girisha GS. 2016. \u201c5M: Multi-Instance Multi-Cluster based Weakly Supervised MIL Model for Multimedia Data Mining\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 16 (GJCST Volume 16 Issue C3): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
The methods for personal identification and authentication are no exception.
Total Score: 107
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: Girisha GS, Dr. K. Udaya Kumar (PhD/Dr. count: 1)
View Count (all-time): 264
Total Views (Real + Logic): 7332
Total Downloads (simulated): 1921
Publish Date: 2016 07, Fri
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Neural Networks and Rules-based Systems used to Find Rational and
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The high pace rise in online as well as offline multimedia un annotated data and associated mining applications have demanded certain efficient mining algorithm. Multiple instance learning (MIL) has emerged as one of the most effective solutions for huge un annotated data mining. Still, it requires enhancement in instance selection to enable optimal mining and classification of huge multimedia data. Considering critical multimedia mining applications, such as medical data processing or content based information retrieval, the instance verification can be of great significance to optimize MIL. With this motivation, in this paper, Multi-Instance, Multi-Cluster based MIL scheme (MIMC-MIL) has been proposed to perform efficient multimedia data mining and classification with huge un annotated data with different features. The proposed system employs soft max approximation techniques with a novel loss factor and inter-instance distance based weight estimation scheme for instance probability substantiation in bags.
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