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Hierarchical Inter Intra Cluster Based Enhanced Efficient Power Saving Adaptive Routing Protocol (e-EPSAR) for MANETs Feasibility and Analysis
GAURAV BANGA, AMAR SINGH ECE Department, ISTK, Kurukshetra University Kurukshetra, Haryana, INDIA CSE Department, ISTK, Kurukshetra University Kurukshetra, Haryana, INDIA
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Abstract: In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labelled positive even if only one of the instances in it falls within the concept. A bag is labelled negative only if all the instances in it are negative. This paper discusses the multi-instance problem using two-level distribution (TLD) algorithm.
Keywords: Multi-Instance Learning, Supervised Learning, Label, Two Level Distributions.
Keywords: Multi-Instance Learning, Supervised Learning, Label, Two Level Distributions.
How to Cite:
[1] GAURAV BANGA, AMAR SINGH ECE Department, ISTK, Kurukshetra University Kurukshetra, Haryana, INDIA CSE Department, ISTK, Kurukshetra University Kurukshetra, Haryana, INDIA, βHierarchical Inter Intra Cluster Based Enhanced Efficient Power Saving Adaptive Routing Protocol (e-EPSAR) for MANETs Feasibility and Analysis,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE)
This work is licensed under a Creative Commons Attribution 4.0 International License.