2 Search Results for "Li, Ximing"

Document
DOI: 10.4230/LIPIcs.ESA.2023.64
Improved Quantum Boosting

Authors: Adam Izdebski and Ronald de Wolf

Published in: LIPIcs, Volume 274, 31st Annual European Symposium on Algorithms (ESA 2023)

Abstract
Boosting is a general method to convert a weak learner (which generates hypotheses that are just slightly better than random) into a strong learner (which generates hypotheses that are much better than random). Recently, Arunachalam and Maity [Srinivasan Arunachalam and Reevu Maity, 2020] gave the first quantum improvement for boosting, by combining Freund and Schapire’s AdaBoost algorithm with a quantum algorithm for approximate counting. Their booster is faster than classical boosting as a function of the VC-dimension of the weak learner’s hypothesis class, but worse as a function of the quality of the weak learner. In this paper we give a substantially faster and simpler quantum boosting algorithm, based on Servedio’s SmoothBoost algorithm [Servedio, 2003].
Cite as

Adam Izdebski and Ronald de Wolf. Improved Quantum Boosting. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{izdebski_et_al:LIPIcs.ESA.2023.64,
  author =	{Izdebski, Adam and de Wolf, Ronald},
  title =	{{Improved Quantum Boosting}},
  booktitle =	{31st Annual European Symposium on Algorithms (ESA 2023)},
  pages =	{64:1--64:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-295-2},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{274},
  editor =	{G{\o}rtz, Inge Li and Farach-Colton, Martin and Puglisi, Simon J. and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2023.64},
  URN =		{urn:nbn:de:0030-drops-187178},
  doi =		{10.4230/LIPIcs.ESA.2023.64},
  annote =	{Keywords: Learning theory, Boosting algorithms, Quantum computing}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.68
A Safety Evaluation Method of Evacuation Routes in Urban Areas in Case of Earthquake Disasters Using Ant Colony Optimization and Geographic Information Systems (Short Paper)

Authors: Kayoko Yamamoto and Ximing Li

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
The present study aims to propose the method for the quantitative evaluation of safety concerning evacuation routes in case of earthquake disasters in urban areas using Ant Colony Optimization (ACO) algorithm and Geographic Information Systems (GIS). Regarding the safety evaluation method, firstly, the similarity in safety was focused on while taking into consideration road blockage probability, and after classifying roads by means of the hierarchical cluster analysis, the congestion rates of evacuation routes using ACO simulations were estimated. Based on these results, the multiple evacuation routes extracted were visualized on digital maps by means of GIS, and its safety was evaluated. Furthermore, the selection of safe evacuation routes between evacuation sites, for cases when the possibility of large-scale evacuation after an earthquake disaster is high, is made possible. As the safety evaluation method is based on public information, by obtaining the same geographic information as the present study, it is effective in other areas regardless of whether the information is of the past and future. Therefore, in addition to spatial reproducibility, the safety evaluation method also has high temporal reproducibility. Because safety evaluations are conducted on evacuation routes based on quantified data, highly safe evacuation routes that are selected have been quantitatively evaluated, and thus serve as an effective indicator when selecting evacuation routes.
Cite as

Kayoko Yamamoto and Ximing Li. A Safety Evaluation Method of Evacuation Routes in Urban Areas in Case of Earthquake Disasters Using Ant Colony Optimization and Geographic Information Systems (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 68:1-68:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

Copy BibTex To Clipboard

@InProceedings{yamamoto_et_al:LIPIcs.GISCIENCE.2018.68,
  author =	{Yamamoto, Kayoko and Li, Ximing},
  title =	{{A Safety Evaluation Method of Evacuation Routes in Urban Areas in Case of Earthquake Disasters Using Ant Colony Optimization and Geographic Information Systems}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{68:1--68:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.68},
  URN =		{urn:nbn:de:0030-drops-93966},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.68},
  annote =	{Keywords: Large-Scale Evacuation, Evacuation Route, Safety Evaluation, Earthquake Disaster, ACO (Ant Colony Optimization), GIS (Geographic Information Systems)}
}
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