A Special Supervised Learning Algorithm and Its Applications

Pang, Yanwei; Chen, Jiqiang; Wang, Nianpeng

doi:10.1109/his.2009.95

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A Semi-supervised Methods1

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2018

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“…Training sample set must be composed of a labeled sample, rather than SSL methods that only analyze the data set itself. If data sets show some kind of aggregation, they can be classified according to the nature of aggregation, but it is not with a pre-classification label for this purpose (Pang et al, 2009;Weng et al, 2008; Zhang et al, 2010).…”

Section: A Semi-supervised Methodsmentioning

confidence: 99%

A New Optimization Approach to Resource Distribution Using Semi-Supervised Learning Graphs

Banisakher

Mohammed

Nguyen

2018

International Journal of Simulation: Systems, Science &Amp; Technology

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An effective Post-Disaster Management System (PDMS) will result in distribution of emergency resources such as, hospital, storage and transportation in a reasonable and equitable manner. This study starts with semi-supervised learning (SSL) based graph system to provide post-disaster path optimizations. Next, the graph-based resource is converted to a directed graph resulting in an adjacent matrix. Decision information is provided in two ways: clustering operation and graph-based semi-supervised optimization. The PDMS in this study incorporates a path optimization algorithm based on Ant Colony Optimization (ACO) that results in costeffective resource distribution. Simulation results demonstrate the effectiveness of the proposed methodology by comparing ACO with clustering based algorithms of tour improvement algorithm (TIA) and Min-Max Ant System (MMAS).

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Section: A Semi-supervised Methodsmentioning

confidence: 99%