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Allowing context to speak: the progressive case study method for cadastral systems research / Simon Hull in Survey review, vol 55 n° 390 (May 2023)  

Public [article]  inSurvey review > vol 55 n° 390 (May 2023) . - pp 205 - 215 Titre : Allowing context to speak: the progressive case study method for cadastral systems research Type de document : Article/Communication Auteurs : Simon Hull, Auteur ; Jennifer Whittal, Auteur Année de publication : 2023 Article en page(s) : pp 205 - 215 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre [Termes IGN] droit coutumier [Termes IGN] droit foncier [Termes IGN] prise en compte du contexte [Termes IGN] raisonnement déductif [Termes IGN] raisonnement inductif Résumé : (auteur) For research involving customary land rights, "context is key" because every context brings specific nuances for consideration. Failure to account for context runs the risk of irrelevance, unintended consequences and/or failure. We present a research method that allows context to speak: the progressive case study. The approach combines deductive case study with inductive grounded theory approaches. The results are used to propose a framework for guiding cadastral systems development in customary land rights contexts. This paper presents the methodology, which should be useful for researchers, NGOs and multinational organisations doing development programming in developing contexts. Numéro de notice : A2023-211 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/00396265.2022.2045457 Date de publication en ligne : 06/03/2022 En ligne : https://doi.org/10.1080/00396265.2022.2045457 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103065 [article]

Cartographic inference: a peircean perspective / Gordon A. Cromley in Cartographica, vol 55 n° 2 (Summer 2020)  

Public [article]  inCartographica > vol 55 n° 2 (Summer 2020) . - pp 124 - 135 Titre : Cartographic inference: a peircean perspective Type de document : Article/Communication Auteurs : Gordon A. Cromley, Auteur Année de publication : 2020 Article en page(s) : pp 124 - 135 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie [Termes IGN] carte analytique [Termes IGN] inférence [Termes IGN] raisonnement abductif [Termes IGN] raisonnement déductif [Termes IGN] raisonnement inductif Résumé : (auteur) A major focus of cartographic research can be framed within two broad trends involving geovisual analytic and critical cartographic approaches. Understated in the development both of scientific and critical approaches to the field of cartography has been the role of cartographic inference. Making inferences from maps is fundamental to the visual analytical tradition and the thinking/communication continuum. Reasoning is also fundamental to critical cartography and the development of critiques relies on inference based on “evidence” encoded or inscribed in a map or set of maps. The social construction of a map and the map’s use have a significant impact on the types of inferences that are made, but conclusions must be carefully scrutinized with respect to these inferences. This study examines the Piercean notions of abductive, deductive, and inductive inference and their application to cartographic inquiry from both scientific and critical perspectives. A study of John Snow’s famous map of a cholera outbreak in London shows the evolution of this map from an instrument of scientific inquiry to one of historical discourse. This historical discourse also shows the continuous unfolding of “Snow’s map” as a mapping practice. By understanding how logical inferences change over time as the context of a map within society changes, this study shows biases inherent within cartographic expression integral to both scientific and critical lines of inquiry. Numéro de notice : A2020-440 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3138/cart-2019-0029 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.3138/cart-2019-0029 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95498 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
031-2020021	SL	Revue	Centre de documentation	Revues en salle	Disponible

An Introduction to Machine Learning / Miroslav Kubat (2017)  

Public Titre : An Introduction to Machine Learning Type de document : Guide/Manuel Auteurs : Miroslav Kubat, Auteur Mention d'édition : 2ème édition Editeur : Springer International Publishing Année de publication : 2017 ISBN/ISSN/EAN : 978-3-319-63913-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] algorithme génétique [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage par renforcement [Termes IGN] apprentissage profond [Termes IGN] arbre de décision [Termes IGN] classificateur [Termes IGN] classification barycentrique [Termes IGN] classification bayesienne [Termes IGN] exploration de données [Termes IGN] raisonnement inductif [Termes IGN] réseau neuronal artificiel [Termes IGN] test de performance Mots-clés libres : Bayesian classifiersboostingcomputational learning theorydecision treesgenetic algorithmslinear and polynomial classifiersnearest neighbor classifierneural networksperformance evaluationreinforcement learningstatistical learningtime-varying classes, imbalanced representationartificial intelligencemachine learningdata miningdeep learningunsupervised learning Résumé : (Auteur) [Introduction] This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. Numéro de notice : 26276 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Manuel DOI : 10.1007/978-3-319-63913-0 En ligne : https://doi.org/10.1007/978-3-319-63913-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94915

A rough set approach to the discovery of classification rules in spatial data / Yee Leung in International journal of geographical information science IJGIS, vol 21 n° 9-10 (october 2007)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 21 n° 9-10 (october 2007) . - pp 1033 - 1058 Titre : A rough set approach to the discovery of classification rules in spatial data Type de document : Article/Communication Auteurs : Yee Leung, Auteur ; T. Fung, Auteur ; et al., Auteur Année de publication : 2007 Article en page(s) : pp 1033 - 1058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] base de règles [Termes IGN] classification [Termes IGN] données localisées [Termes IGN] exploration de données géographiques [Termes IGN] image Ikonos [Termes IGN] outil de découverte de connaissances [Termes IGN] raisonnement inductif [Termes IGN] système d'information géographique Résumé : (Auteur) This paper proposes a novel rough set approach to discover classification rules in real-valued spatial data in general and remotely sensed data in particular. A knowledge induction process is formulated to select optimal decision rules with a minimal set of features necessary and sufficient for a remote sensing classification task. The approach first converts a real-valued or integer-valued decision system into an interval-valued information system. A knowledge induction procedure is then formulated to discover all classification rules hidden in the information system. Two real-life applications are made to verify and substantiate the conceptual arguments. It demonstrates that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task. It is also capable of unraveling critical spectral band(s) discerning certain classes. The framework paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data. Copyright Taylor & Francis Numéro de notice : A2007-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810601169915 En ligne : https://doi.org/10.1080/13658810601169915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28918 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-07061	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-07062	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Evaluation of the horizontal resolution of SRTM elevation data / L. Pierce in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 11 (November 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 11 (November 2006) . - pp 1235 - 1244 Titre : Evaluation of the horizontal resolution of SRTM elevation data Type de document : Article/Communication Auteurs : L. Pierce, Auteur ; J. Kelindorf, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 1235 - 1244 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie spatiale [Termes IGN] lever topographique [Termes IGN] limite de résolution géométrique [Termes IGN] MNS SRTM [Termes IGN] modèle numérique de surface [Termes IGN] précision géométrique (imagerie) [Termes IGN] qualité géométrique (image) [Termes IGN] raisonnement inductif Résumé : (Auteur) The SRTM dataset is available at the USGS seamless website with one arc-second pixel spacing for the U.S. Recently, the value for horizontal resolution has been questioned. One paper (Smith and Sandwell, 2003) suggests that 60 meters may be more accurate, implying that the resolution is twice the provided spacing. For users of this data, the horizontal resolution is very important for their analyses. Hence, this paper addresses this important question by using two different approaches: coherence spectra and step-response. The coherence spectra approach uses statistical techniques to compare the SRTM dataset against a more accurate one, while the step response approach uses the observed step response in many areas of the dataset to estimate the width of the averaging function used to produce the SRTM data. Results from this study show that the resolution is between 1 and 1.6 pixels, depending on the local variability of the elevation data; with higher resolution near sharp edges and corners, and lower resolution in smoother areas. Copyright ASPRS Numéro de notice : A2006-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.11.1235 En ligne : https://doi.org/10.14358/PERS.72.11.1235 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28216 [article]

Linking geomatics and participatory social analysis for environmental monitoring: case studies from Malawi / M. Snel in Cartographica, vol 37 n° 4 (December 2000)  

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Apprentissage automatique / Marc Sebban (1999)

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