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A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks / Lin Wan in International journal of geographical information science IJGIS, vol 32 n° 11-12 (November - December 2018)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 32 n° 11-12 (November - December 2018) . - pp 2225 - 2246 Titre : A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks Type de document : Article/Communication Auteurs : Lin Wan, Auteur ; Yuming Hong, Auteur ; Zhou Huang, Auteur ; Xia Peng, Auteur ; Ran Li, Auteur Année de publication : 2018 Article en page(s) : pp 2225 - 2246 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] calcul d'itinéraire [Termes IGN] classification bayesienne [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données localisées des bénévoles [Termes IGN] données météorologiques [Termes IGN] exploration de données géographiques [Termes IGN] géobalise [Termes IGN] image Flickr [Termes IGN] Pékin (Chine) [Termes IGN] point d'intérêt Résumé : (Auteur) Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns. Numéro de notice : A2018-523 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1458988 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1458988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91348 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2018061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Using hachures to construct a 3D doline model automatically / Nai Yang in Cartographica, vol 50 n° 2 (Summer 2015)  

Public [article]  inCartographica > vol 50 n° 2 (Summer 2015) . - pp 86 - 93 Titre : Using hachures to construct a 3D doline model automatically Type de document : Article/Communication Auteurs : Nai Yang, Auteur ; Lin Wan, Auteur ; G. Zheng, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 86 - 93 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Rédaction cartographique [Termes IGN] effondrement de terrain [Termes IGN] grotte [Termes IGN] hachure [Termes IGN] représentation cartographique 3D Résumé : (auteur) All types of relief features are usually represented by a unified digital terrain model on 3D topographic maps. Few studies have considered the differences in relief characteristics and terrain complexity. Hachures are often used to construct the relief symbols on 2D topographic maps, but they are less used on 3D topographic maps. This article uses hachures to construct a 3D doline model and divides them into three types: outlines, ridge lines, and break lines. We present the extraction method for doline outlines, consider the rules of visual perception under different illumination conditions, and study the mathematical models for representing the morphological characteristics of the doline with regard to the following aspects: width, arrangement density, and grey value of hachures. Finally, we introduce a process for 3D modelling of dolines on the basis of the above discussion. Experimental results indicate that the proposed model achieves a good 3D visual representation of dolines. Furthermore, the proposed model can also be used as a reference for the creation of 3D models of other negative relief features. Numéro de notice : A2015-273 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.3138/cart.50.2.2793 En ligne : http://www.utpjournals.press/doi/full/10.3138/cart.50.2.2793 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76386 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
031-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible