Mikhail Kanevski

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181 publications

2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 |
A novel framework for spatio-temporal prediction of environmental data using deep learning
Amato Federico, Guignard Fabian, Robert Sylvain, Kanevski Mikhail, 2020/12. Scientific Reports, 10 (1).
Spatio-temporal evolution of global surface temperature distributions
Amato Federico, Guignard Fabian, Humphrey Vincent, Kanevski Mikhail, 2020/09/22. Proceedings of the 10th International Conference on Climate Informatics pp. 37-43. Peer-reviewed.
Advanced Analysis of Temporal Data Using Fisher-Shannon Information: Theoretical Development and Application in Geosciences
Guignard Fabian, Laib Mohamed, Amato Federico, Kanevski Mikhail, 2020/07/14. Frontiers in Earth Science, 8.
 
Analysis of air pollution time series using complexity-invariant distance and information measures
Amato Federico, Laib Mohamed, Guignard Fabian, Kanevski Mikhail, 2020/02. Physica A: Statistical Mechanics and its Applications p. 124391. Peer-reviewed.
 
Analysis of temporal properties of extremes of wind measurements from 132 stations over Switzerland
Telesca Luciano, Guignard Fabian, Laib Mohamed, Kanevski Mikhail, 2020/01. Renewable Energy. Peer-reviewed.
Wavelet Scale Variance Analysis of Wind Extremes in Mountainous Terrains
Telesca Luciano, Guignard Fabian, Helbig Nora, Kanevski Mikhail, 2019/08/07. Energies, 12 (16) p. 3048. Peer-reviewed.
 
Wavelet variance scale-dependence as a dynamics discriminating tool in high-frequency urban wind speed time series
Guignard Fabian, Mauree Dasaraden, Kanevski Mikhail, Telesca Luciano, 2019/07. Physica A: Statistical Mechanics and its Applications, 525 pp. 771-777. Peer-reviewed.
Community detection analysis in wind speed-monitoring systems using mutual information-based complex network
Laib Mohamed, Guignard Fabian, Kanevski Mikhail, Telesca Luciano, 2019/04. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29 (4) p. 043107. Peer-reviewed.
 
Linearity versus non-linearity in high frequency multilevel wind time series measured in urban areas
Telesca Luciano, Laib Mohamed, Guignard Fabian, Mauree Dasaraden, Kanevski Mikhail, 2019/03. Chaos, Solitons & Fractals, 120 pp. 234-244. Peer-reviewed.
 
Investigating the time dynamics of wind speed in complex terrains by using the Fisher–Shannon method
Guignard Fabian, Lovallo Michele, Laib Mohamed, Golay Jean, Kanevski Mikhail, Helbig Nora, Telesca Luciano, 2019/02. Physica A: Statistical Mechanics and its Applications, 523 pp. 611-621. Peer-reviewed.
 
Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines
Walch Alina, Castello Roberto, Mohajeri Nahid, Guignard Fabian, Kanevski Mikhail, Scartezzini Jean-Louis, 2019/02. Energy Procedia, 158 pp. 6378-6383.
Fisher–Shannon Complexity Analysis of High-Frequency Urban Wind Speed Time Series
Guignard Fabian, Mauree Dasaraden, Lovallo Michele, Kanevski Mikhail, Telesca Luciano, 2019/01/10. Entropy, 21 (1) p. 47. Peer-reviewed.
 
Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal
Amato Federico, Tonini Marj, Murgante Beniamino, Kanevski Mikhail, 2018/06. Environmental Modelling & Software, 104 pp. 171-187.
 
Multifractal analysis of the time series of daily means of wind speed in complex regions
Laib M., Golay J., Telesca L., Kanevski M., 2018/04. Chaos, Solitons & Fractals, 109 pp. 118-127. Peer-reviewed.
 
Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network
Laib M., Telesca L., Kanevski M., 2018/03. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28 p. 033108.
 
Wildfire susceptibility mapping: Deterministic vs. stochastic approaches
Leuenberger Michael, Parente Joana, Tonini Marj, Pereira Mário Gonzalez, Kanevski Mikhail, 2018/03. Environmental Modelling & Software, 101 pp. 194-203.
 
Periodic fluctuations in correlation-based connectivity density time series: Application to wind speed-monitoring network in Switzerland
Laib M., Telesca L., Kanevski M., 2018/02. Physica A: Statistical Mechanics and its Applications, 492 pp. 1555-1569. Peer-reviewed.
A novel filter algorithm for unsupervised feature selection based on a space filling measure
Laib Mohamed, Kanevski Mikhail (eds.), 2018., 26rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) 485.
 
Feature selection for regression problems based on the Morisita estimator of intrinsic dimension
Golay J., Leuenberger M., Kanevski M., 2017/05/10. Pattern Recognition, 70 pp. 126-138. Peer-reviewed.
Data-driven mapping of the potential mountain permafrost distribution
Deluigi N., Lambiel C., Kanevski M., 2017/03/08. Science of the Total Environment, 590-591 pp. 370–380. Peer-reviewed.
 
Unsupervised feature selection based on the Morisita estimator of intrinsic dimension
Golay J., Kanevski M., 2017. Knowledge-Based Systems, 135 pp. 125-134. Peer-reviewed.
 
Spatial Modelling of Extreme Wind Speed Distributions in Switzerland
Laib M., Kanevski M., 2016/11. Energy Procedia, 97 pp. 100-107. Peer-reviewed.
 
Comparing seismicity declustering techniques by means of the joint use of Allan Factor and Morisita index
Telesca L., Lovallo M., Golay J., Kanevski M., 2016. Stochastic Environmental Research and Risk Assessment, 30 pp. 77-90. Peer-reviewed.
 
A new estimator of intrinsic dimension based on the multipoint Morisita index
Golay J., Kanevski M., 2015/12. Pattern Recognition, 48 (12) pp. 4070-4081. Peer-reviewed.
 
Multifractal portrayal of the Swiss population
Vega Orozco C. D., Golay J., Kanevski M., 2015/03. Cybergeo : European Journal of Geography [En ligne] pp. 1-16. Peer-reviewed.
 
Morisita-based feature selection for regression problems
Golay J., Leuenberger M., Kanevski M., 2015. pp. 279-284 dans Proceedings of the 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). Peer-reviewed, d-side pub.
 
Morisita-based space-clustering analysis of Swiss seismicity
Telesca L., Golay J., Kanevski M., 2015. Physica A: Statistical Mechanics and its Applications, 419 pp. 40-47. Peer-reviewed.
 
The multipoint Morisita index for the analysis of spatial patterns
Golay J., Kanevski M., Vega Orozco C. D., Leuenberger M., 2014. Physica A: Statistical Mechanics and its Applications, 406 pp. 191-202. Peer-reviewed.
 
Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping
Micheletti Natan, Foresti Loris, Robert Sylvain, Leuenberger Michael, Pedrazzini Andrea, Jaboyedoff Michel, Kanevski Mikhail, 2013/12. Mathematical Geosciences, 46 (1) pp. 33-57. Peer-reviewed.
 
A methodology for analysis and modelling of spatial environmental data
Kanevski M., 2013. pp. 105-107 dans GEOProcessing 2013: The Fifth International Conference on Advanced Geographic Information Systems, Applications, and Services, Nice, France. Peer-reviewed, Think Mind.
 
Flooding extent cartography with Landsat TM imagery and regularized kernel Fisher's discriminant analysis
Volpi M., Petropoulos G.P., Kanevski M., 2013. Computers & Geosciences, 57 pp. 24-31.
 
Multi-view feature extraction for hyperspectral image classification
Volpi M., Matasci G., Kanevski M., Tuia D., 2013. pp. 11-16 dans European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning ESANN, Bruges (B). Peer-reviewed.
 
Multifractal portrayal of the Swiss population
Vega-Orozco C., Golay J., Kanevski M., 2013. ArXiv preprint pp. 1-17.
 
Multisensor change detection with nonlinear canonical correlation
Volpi M., De Morsier F., Camps-Valls G., Kanevski M., Tuia D., 2013. dans IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia.
 
Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks
Robert S., Foresti L., Kanevski M., 2013. International Journal of Climatology, 33 pp. 1793-1804. Peer-reviewed.
 
Statistical assessment of dataset shift and model portability in multi-angle in-track image acquisitions
Matasci G., Longbotham N., Pacifici F., Kanevski M., Tuia D., 2013. pp. 4134 - 4137 dans Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International.
 
Supervised change detection in VHR images using contextual information and support vector machines
Volpi M., Tuia D., Bovolo F., Kanevski M., Bruzzone L., 2013. International Journal of Applied Earth Observation and Geoinformation, 20 pp. 77-85.
 
The multipoint Morisita index for the analysis of spatial patterns
Golay J., Kanevski M., Vega Orozco C., Leuenberger M., 2013. arXiv 1307.3756 pp. 1-18.
 
A geomatic approach to Wildland-Urban interface detection
Champendal A., Vega-Orozco C., Ceré R., Kanevski M., Tonini M., 2012. pp. 73-76 dans Billen R., Binard M., Hallot P., Donnay J.P. (eds.) Proceedings of Spatial Analysis and GEOmatics 2012, Unité de Géomatique, Université de Liège, Belgium.
 
Active learning for monitoring network optimization
Tuia D., Pozdnoukhov A., Foresti L., Kanevski M., 2012. pp. 285-318 dans Mateu J., Mueller W.G. (eds.) Spatio-temporal design: Advances in efficient data acquisition chap. 13.
Cluster recognition in spatial-temporal sequences: The case of forest fires
Vega-Orozco C., Tonini M., Conedera M., Kanevski M., 2012. Geoinformatica, 16 pp. 653-673. Peer-reviewed.
 
Enhanced Change Detection Using Nonlinear Feature Extraction
Volpi M., Matasci G., Tuia D., Kanevski M., 2012. dans Proc. IEEE Int. Geosci. Remote Sens. Symp. IGARSS, Munich, Germany.
 
Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar
Foresti L., Kanevski M., Pozdnoukhov A., 2012. IEEE Transactions on Geoscience and Remote Sensing, 50 pp. 2954 - 2967. Peer-reviewed.
 
Memory-based cluster sampling for remote sensing image classification
Volpi M., Tuia D., Kanevski M., 2012. IEEE Transactions on Geoscience and Remote Sensing, 50 pp. 3096-3106.
 
Multifractal portrayal of the distribution of the Swiss population
Vega-Orozco C., Golay J., Kanevski M., 2012. pp. 392-407 dans Billen R., Binard M., Hallot P., Donnay J.P. (eds.) Proceedings of Spatial Analysis and GEOmatics, Unité de Geomatique, Université de Liège, Belgium.
 
Multitask learning of environmental spatial data
Kanevski M., 2012., International Environmental Modelling and Software Society pp. 1594-1602 dans Seppelt R., Voinov A.A., Lange S., Bankamp D. (eds.) International Congress on Environmental Modelling and Software: Managing resources of a limited planet, sixth biennial meeting, Leipzig, Germany.
 
Spatial patterns analysis of environmental data using R
Golay J., Vega Orozco C., Tonini M., Kanevski M., 2012. pp. 245-252 dans Ertz O., Joost S., Tonini M. (eds.) Open Source Geospatial Research & Education Symposium, Yverdon-les-Bains, Switzerland.
 
Unsupervised change detection with kernels
Volpi M., Tuia D., Camps-Valls G., Kanevski M., 2012. IEEE Geoscience and Remote Sensing Letters, 9 pp. 1026-1030.
 
Using active learning for monitoring networks design: The example of wind power plants sites evaluation
Tuia D., Pozdnoukhov A., Kanevski M., 2012. pp. 6 p. dans 9th International Geostatistics Congress, Oslo, Norway.
Transfer component analysis for domain adaptation in image classification
Matasci Giona, Volpi Michele, Tuia Devis, Kanevski Mikhail, 2011/10/06. dans Image and Signal Processing for Remote Sensing XVII, SPIE.
 
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
Tuia D., Volpi M., Copa L., Kanevski M., 2011. IEEE Journal of Selected Topics in Signal Processing, 5 pp. 606-617. Peer-reviewed.
Analysis, Modeling and Spatio-Temporal Prediction of Avalanches Using Support Vector Machines
Matasci G., Pozdnoukhov A., Kanevski M., 2011. dans Proc. of the World Statistics Congress of the International Statistical Institute (ISI) 2011, Dublin, Ireland.
 
Artificial Snow Optimization in Winter Sport Destinations Using a Multi-agent Simulation
Revilloud M., Loubier J -C., Doctor M., Kanevski M., Timonin V., Schumacher M., 2011. pp. 201-210 dans Demazeau Y., Pechoucek M., Corchado J.M., Pérez J.B. (eds.) Advances on Practical Applications of Agents and Multiagent Systems: 9th International Conference on Practical Applications of Agents and Multiagent Systems, Salamanca, Spain, Springer Berlin Heidelberg.
 
Automatic Mapping and Classification of Spatial Environmental Data
Kanevski M., Timonin V., Pozdnoukhov A ., 2011. Geocomputation, Sustainability and Environmental Planning, 348 pp. 205-223. Peer-reviewed.
Domain Separation For Efficient Adaptive Active Learning
G. Matasci , D. Tuia , M. Kanevski , 2011. dans Proc. IEEE IGARSS 2011, Vancouver, Canada.
 
Embedding and retrieval of weather radar sequences: A data mining approach to precipitation nowcasting
Foresti L., Kanevski M., 2011. dans International Symposium on Spatial-Temporal Analysis and Data Mining, London, England.
 
Evaluation area-based crime prevention programs using geospatial data mining
Kreis C., Kanevski M., Kuhn A., 2011. dans International Symposium on Spatial-Temporal Analysis and Data Mining, University College London, England.
 
Exploratory analysis of forest fires spatial distribution using multivariate variography
Vega-Orozco C., Kanevski M., Tonini M., Conedera M., 2011. dans Antoni J.P. (eds.) Dixièmes rencontres de Théo Quant, Besançon, Laboratoire ThéMA Unité Mixte de Recherche (UMR 6049) - CNRS - Université de Franche-Comté.
Learning wind fields with multiple kernels
Foresti L., Tuia D., Kanevski M., Pozdnoukhov A., 2011. Stochastic Environmental Research and Risk Assessment, 25 pp. 51-66. Peer-reviewed.
 
Pattern mining in spatial-temporal sequences: The case of forest fires in Ticino (Switzerland)
Vega Orozco C., Kanevski M., Tonini M., Conedera M., 2011. dans International Symposium on Spatial-Temporal Analysis and Data Mining, University College London, United Kingdom.
 
Point pattern analysis of spatio-temporal clustering for forest fire occurrences in Ticino (Switzerland)
Vega-Orozco C., Tonini M., Kanevski M., Conedera M., 2011. pp. 126-127 dans Duce P., Spano D. (eds.) International Conference on Fire Behaviour and Risk Focus on Wildland Urban Interface, Sassari, Italy, Department of Economics and Woody Plant Systems (DESA), University of Sassari; National Research Council of Italy, Institute of Biometeorology (CNR-IBIMET).
Spatio-temporal avalanche forecasting with Support Vector Machines
Pozdnoukhov A., Matasci G., Kanevski M., Purves R. S., 2011. Natural Hazards and Earth System Science, 11 (2) pp. 367-382. Peer-reviewed.
 
Advanced active sampling for remote sensing image classification
Volpi M., Tuia D., Kanevski M., 2010. pp. 1414-1417 dans Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Peer-reviewed.
 
Analysis, modelling and classification of geospatial data using machine learning
Kanevski M., Timonin V., Foresti L., Tanadini M., 2010. pp. 1-14 dans Proccedings of the Annual Conference of the International Association for Mathematical Geosciences, Budapest, Hungary, 29 August - 2 September. Peer-reviewed.
 
Cluster-based active learning for compact image classification
Tuia D., Kanevski M., Munoz Mari J., Camps-Valls G., 2010. pp. 2824-2827 dans Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Honolulu, United States of America, IEEE Conference Publications.
 
Correction to "Active learning methods for remote sensing image classification" [Jul 09 2218-2232]
Tuia D., Ratle A., Pacifici F., Kanevski M., 2010. IEEE Transactions on Geoscience and Remote Sensing, 48 p. 2767. Peer-reviewed.
 
Detection of Optimal Models in Parameter Space with Support Vector Machines
Demyanov V., Pozdnoukhov A., Christie M., Kanevski M., 2010. pp. 345-358 dans Lloyd C.D., Atkinson P.M. (eds.) Geostatistics for Environmental Applications VII. Peer-reviewed, Springer Netherlands.
Learning Relevant Image Features With Multiple-Kernel Classification
Tuia D., Camps-Valls G., Matasci G., Kanevski M., 2010. IEEE Transactions on Geoscience and Remote Sensing, 48 (10) pp. 3780 -3791. Peer-reviewed.
 
Machine learning analysis and modeling of interest rate curves
Kanevski M., Timonin V., 2010. pp. 47-52 dans European Symposium on Artificial Neural Networks: Computational intelligence and machine learning, Bruges, Belgium.
 
Multiple Kernel Models for Reservoir Characterization
Foresti L., Demyanov V., Christie M., Kanevski M., 2010. pp. 47-49 dans Book of Abstracts of geoENV 2010, 8th International Conference on Geostatistics for Environmental Applications, Gand, Belgium.
 
Optimization of snowmaking in high mountains ski resort
Loubier J.C., Kanevski M., Schumacher M., Timoni V., Claret S., Zieba A., Marrut D., 2010. pp. 1-9 dans Painho M., Santos M.Y., Pundt H. (eds.) Proceedings of the 13th AGILE International Conference on Geographic Information Science, Guimaraes, Portugal, AGILE.
 
Pattern recognition in environmental data using general regression neural networks
Kanevski M., Timonin V., Robert S., Foresti L., 2010. pp. 114-116 dans Book of Abstracts of geoENV 2010, 8th International Conference on Geostatistics for Environmental Applications.
Structured Output SVM for Remote Sensing Image Classification
Tuia D., Muñoz-Marí J., Kanevski M., Camps-Valls G., 2010. Journal of Signal Processing Systems, 65 pp. 301-310. Peer-reviewed.
 
Temporal patterns of fire sequences observed in Canton of Ticino (southern Switzerland)
Telesca M., Kanevski M., Tonini G., Pezzatti B., Conedera M., 2010. Natural Hazards Earth System Science, 10 pp. 723-728. Peer-reviewed.
 
Time Series Input Selection using Multiple Kernel Learning
Foresti L., Tuia D., Timonin V., Kanevski M., 2010. pp. 123-128 dans European symposium on artificial neural network ESANN, Computational Intelligence and Machine Learning, Bruges, Belgium. Peer-reviewed.
 
Unbiased query-by-bagging active learning for VHR image classification
Copa L., Tuia D., Volpi M., Kanevski M., 2010. pp. 78300K-78300K-8 dans Proceedings of SPIE 7830, Image and Signal Processing for Remote Sensing XVI.
 
Uncertainty Quantification with Support Vector Regression Prediction Models
Demyanov V., Pozdnoukhov A., Kanevski M., Christie M., 2010. pp. 133-136 dans Proceedings of the Accuracy conference, Leicester, England, International Spatial Accuracy Research Association.
 
Unsupervised change detection by kernel clustering
Volpi M., Tuia D., Camps-Valls G., Kanevski M., 2010. pp. 78300V-78300V-8 dans Proceedings of SPIE 7830, Image and Signal Processing for Remote Sensing XVI. Peer-reviewed.
 
Unsupervised change detection in the feature space using kernels
Volpi M., Tuia D., Camps-Valls G., Kanevski M., 2010. pp. 106-109 dans Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
 
Classification of very high spatial resolution imagery using mathematical morphology and support vector machines
Tuia D., Pacifici F., Kanevski M., Emery W. J., 2009/11. IEEE Transactions on Geoscience and Remote Sensing, 47 (11) pp. 3866-3879. Peer-reviewed.
 
Active learning methods for remote sensing image classification
Tuia D., Ratle F., Pacifici F., Kanevski M., Emery W.J., 2009/07. IEEE Transactions on Geoscience and Remote Sensing, 47 (7) pp. 2218-2232. Peer-reviewed.
 
Machine learning for spatial environmental data. Theory, applications and software
EPFL Press (eds.)Mikhail Kanevski, Alexei Pozdnoukhov, Vadim Timonin, 2009/05. 368, EPFL Press.
 
Analysis and modelling of snow depth patterns in alpine ski resorts
Timonin V., Kanevski M., Loubier J.-C., Doctor M., 2009. dans European Colloquium on Quantitative and Theoretical Geography, Maynooth, Ireland, 4-8 September. Peer-reviewed, ISC-PIF Open Multimedia Library.
 
ANNEX model: Artificial Neural Networks with External Drift Environmental Data Mapping
Parkin R., Kanevski M., 2009. pp. 57-64 dans Pilz J. (eds.) Interfacing Geostatistics and GIS, Springer Berlin Heidelberg.
 
Clustering and hot spot detection in socio-economic spatio-temporal data
Tuia Devis, Kaiser Christian, Da Cunha Antonio, Kanevski Mikhail, 2009. pp. 234-250 dans Gavrilova M., Kenneth Tan C.J. (eds.) Transactions on Computational Science VI, Springer.
Data-driven topo-climatic mapping with machine learning methods
Pozdnoukhov A., Foresti L., Kanevski M., 2009. Natural Hazards, 50 pp. 497 - 518. Peer-reviewed.
 
Detection of Patterns in Multivariate Financial Data
Kanevski M., Timonin V., Maignan M., Pozdnoukhov A., 2009. dans Hitotsubashi Interdisciplinary Conference New Approaches to the Analysis of Large-Scale Business and Economic Data, APFA, Tokyo, Japan, Springer Berlin Heidelberg.
 
Detection of urban socio-economic patterns using clustering techniques
Tuia D., Kaiser C., Kanevski M., Da Cunha A., 2009. pp. 19-37 dans Murgante B., Borruso G., Lapucci A. (eds.) Geocomputation and Urban Planning, Springer.
 
Emergence of Swiss metropole and scaling properties of urban clusters
Kaiser Christian, Kanevski Mikhail, Da Cunha Antonio, Timonin Vadim, 2009. dans S4 International Conference on Emergence in Geographical Space, 23-25 November. Peer-reviewed.
Learning the relevant image features with multiple kernels
D. Tuia , G. Matasci , M. Kanevski , G. Camps-Valls , 2009. dans Proc. IEEE IGARSS 2009, Cape Town, South Africa.
 
Machine Learning Algorithms for Spatial Categorical Data: Classification, Prediction, and Monitoring Networks Optimization
Kanevski M., Pozdnoukhov A., Timonin V., 2009. dans International Federation of Classification Societies 2009 Conference, Dresden, Germany.
 
Machine learning models for geospatial data
Kanevski Mikhail, Foresti Loris, Kaiser Christian, Pozdnoukhov Alexei, Timonin Vadim, Tuia Devis, 2009. pp. 175-227 dans Bavaud François, Mager Christophe (eds.) Handbook of Theoretical and Quantitative Geography, University, Faculty of geosciences and environment.
 
Machine Learning of Interest Rates
Kanevski M., Timonin V., Pozdnoukhov A., Maignan M., 2009. dans Econophysics conference, Erice, Italy.
 
Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields
Foresti L., Tuia D., Pozdnoukhov A., Kanevski M., 2009. pp. 933-943 dans Alippi C., Polycarpou M., Panayiotou C., Ellinas G. (eds.) 19th international conference on Artificial Neural Network ICANN, Limassol, Cyprus, 5769, Springer Berlin Heidelberg.
 
Regional Classification of Indoor Radon Data with Support Vector Machines and Geostatistical Tools
Chaouch A., Kanevski M., Pozdnoukhov A., Maignan M., Rodriguez J., Piller G., 2009. pp. 65-77 dans Pilz J. (eds.) Interfacing Geostatistics and Geographic information system, Springer Berlin Heidelberg.
 
Sensitivity Analysis of a Spatio-Temporal Avalanche Forecasting Model Based on Support Vector Machines
Matasci G., Pozdnoukhov A., Kanevski M., 2009. dans European Geosciences Union General Assembly 2009, Vienna, Austria.
 
Structured output SVM for remote sensing image classification
Tuia D., Kanevski M., Muñoz-Mari J., Camps-Valls G., 2009. pp. 1-6 dans IEEE International Workshop on Machine Learning for Signal Processing, IEEE Conference Publications.
 
Supervised Change Detection in VHR Images : a Comparative Analysis
Volpi M., Tuia D., Kanevski M., Bovolo F., Bruzzone L., 2009. pp. 1-6 dans IEEE Workshop on Machine Learning for Signal Processing (MLSP) (to appear).
 
Swiss metropole: analysis and geovisualisation of population and service clustering
Kaiser C., Kanevski M., Da Cunha A., 2009., Högskolan I Gävle dans 3rd ICA Workshop on Geospatial Analysis and Modeling, August 6-7, 2009.
 
Uncertainty Quantification Of A Semi-Supervised Support Vector Regression Reservoir Model
Demyanov V., Pozdnoukhov A., Christie M., Kanevski M., 2009. dans International Association for Mathematical Geology Meeting 2009, Leicester, England.
 
Validation of Logistic Regression Models for Landslide Susceptibility Maps
Bai S.B., Wang J., Pozdnoukhov A., Kanevski M., 2009. pp. 355-358 dans Burgin M., Chowdhury M.H., Ham C.H., Ludwig S., Su W., Yenduri S. (eds.) Proceedings of the 2009 World congress on Computer Science and Information Engineering. Peer-reviewed, IEEE Computer Society.
 
Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy
Kanveski M. (eds.)Kanevski M., 2008., 5072 328, ISTE Ltd and Wiley Press.
 
Advanced Mapping of Environmental Spatial Data: Case Studies
Foresti L., Pozdnoukhov A., Kanevski M., Timonin V., Savelieva E., Kaiser C., Tapia R., Purves R., 2008. pp. 149-246 dans Kanevski M. (eds.) Advanced Mapping of Environmental Data. Geostatistics, Machine Learning and Bayesian Maximum Entropy, ISTE, Wiley.
 
Applying machine learning methods to avalanche forecasting
Pozdnoukhov A., Purves R.S., Kanevski M., 2008. Annals of Glaciology, 49 pp. 107-113. Peer-reviewed.
 
Automatic Decision-Oriented Mapping of Pollution Data
Kanevski M., Timonin V., Pozdnoukhov A., 2008. pp. 678-691 dans Gervasi O., Murgante B., Laganà A., Taniar D., Mun Y., Gavrilova M. (eds.) Computational Science and Its Applications: International conference, Perugia, Italy, Proceedings, Part I. Peer-reviewed, Springer Berlin Heidelberg.
 
Clustering in Environmental Monitoring Networks: Dimensional Resolutions and Pattern Detection
Tuia Devis, Kaiser Christian, Kanevski Mikhail, 2008. pp. 497-509 dans Soares Amílcar, Pereira Maria João, Dimitrakopoulos Roussos (eds.) geoEnv VI - Geostatistics for environmental applications. Proceedings of the Sixth European Conference on Geostatistics for Environmental Applications, Quantitative Geology and Geostatistics.
 
Emergence of spatio-temporal patterns in forest-fire sequences
Tuia D., Lasaponara R., Telesca L., Kanevski M., 2008. Physica A, 387 (13) pp. 1689-1694.
 
Entropy-based active learning for very-high resolution imagery
Tuia D., Ratle F., Pacifici F., Pozdnoukhov A., Kanevski M., Del Frate F., Solimini D., Emery W.J ., 2008. dans ESA-EUSC: Image information mining: Pursuing automation of geospatial intelligence for environment and security, Boston, United States of America.
 
Environmental monitoring network characterization and clustering
Tuia D., Kanevski M., 2008. pp. 19-46 dans Kanevski M. (eds.) Advanced Mapping of Environmental Data: Geostatistics, Machine Learning and Bayesian Maximum Entropy chap. 2, ISTE Ltd and Wiley Press.
 
GeoKernels: Modeling of Spatial Data on GeoManifolds
Pozdnoukhov A., Kanevski M., 2008. pp. 277-282 dans European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN, Bruges, Belgium.
 
Geomodelling of a fluvial system with semi-supervised support vector regression
Demyanov V., Pozdnoukhov A ., Kanevski M., Christie M., 2008. pp. 627-636 dans Proceedings of the 8th International Geostatistics Congress, Santiago, Chile, Gecamin Ltd..
 
Geostatistics: Spatial predictions and simulations
Savelieva E, Demyanov V., Maignan M., 2008. pp. 47-94 dans Kanevski M. (eds.) Advanced mapping of environmental data: Geostatistics, and bayesian maximum entropy, ISTE Ltd and Wiley Press.
 
Indicator kriging and sequential gaussian simulations for probability mapping, indoor radon case study
Tapia R., 2008. pp. 222-237 dans Kanevski M. (eds.) Advanced Mapping of Environmental Data: Geostatistics, machine learning, and Bayesian maximum entropy chap. 5.6, ISTE Ltd and Wiley Press.
 
Indoor radon distribution in Switzerland: lognormality and Extreme Value Theory.
Tuia D., Kanevski M., 2008. Journal of Environmental Radioactivity, 99 (4) pp. 649-657.
 
Interest rates mapping
Kanevski M., Maignan M., Pozdnoukhov A., Timonin V., 2008. Physica A - Statistical Mechanics and its Applications, 387 pp. 3897-3903. Peer-reviewed.
 
Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources
Kanevski M., Pozdnoukhov A., Timonin V., 2008. pp. 250-252 dans 6th Swiss Geoscience Meeting, Lugano, Switzerland.
Multi-scale support vector algorithms for hot spot detection and modelling
Pozdnukhov A., Kanevski M., 2008. Stochastic Environmental Research and Risk Assessment, 22 pp. 647-660. Peer-reviewed.
 
Scan statistics analysis of forest fire cluster
Tuia, D., Ratle, F., Lasaponara, R., Telesca, L., Kanevski, M. , 2008. Communications in Nonlinear Science and Numerical Simulation, 13 (8) pp. 1689-1694.
 
Scan statistics analysis of forest fire clusters
Tuia D., Ratle F., Lasaponara R., Telesca L., Kanevski M., 2008. Communications in Nonlinear science and Numerical Simulation, 13 pp. 1689-1694. Peer-reviewed.
 
Socio-economic data analysis with scan statistics and self-organizing maps
Tuia Devis, Kaiser Christian, Da Cunha Antonio, Kanevski Mikhail, 2008. dans Gervasi O., Murgante Beniamino, Lagana A., Taniar D., Mun Y., Gavrilova M. (eds.) Computational Science and Its Applications - ICCSA 2008, Springer.
 
Support-based Implementation of Bayesian Data Fusion for Spatial Enhancement : Applications to ASTER Thermal Images
Fasbender D., Tuia D., Kanevski M., Bogaert P., 2008. IEEE Geosciences and Remote Sensing Letters, 5 (4) pp. 596-602.
 
Bayesian data fusion for image enhancement: An application for thermal infrared ASTER sensors
Tuia D., Fasbender D., Bogaert P., Kanevski M., 2007. dans URBAN/URS joint event 2007, Paris, France.
 
Classification and Visualization of High-Dimensional Socio-Economic Data Using Self-Organizing Maps
Kaiser Christian, Kanevski Mikhail, 2007. dans Spatial Econometrics Conference, University of Cambridge, UK, 11-14 July 2007.
 
Classification of Interest Rates Curves Using Gaussian Mixture Model
Kanevski M., Maignan M., Timonin V., Pozdnoukhov A., 2007. p. 8 dans Econophysics Colloquium and beyond, Ancona, Italy, 27-29 September.
 
GeoKernels: Modelling of spatial data on natural manifolds
Pozdnoukhov A., Kanevski M., Maignan M., 2007. dans European Colloquium on Theoretical and Quantitative Geography, Montreux, Switzerland, 3-7 September.
 
Geostatistical uncertainty quantification for indoor radon risk mapping
Tapia R., Kanevski M., Gruson M., 2007. pp. 379-383 dans 15th European Colloquium of Theoretical and Quantitative Geography, Montreux, Switzerland.
 
Identifying spatial clustering phenomena in forest-fire sequences
Tuia, D., Lasaponara, R., Telesca, L., Kanevski, M. , 2007. Physica A, 376 pp. 596-600.
 
Interactive monitoring network optimization using support vector machines
Pozdnoukhov A., Kanevski M., 2007. dans Spatial Statistics and GIS conference (stat-GIS 2007), Klagenfurt, Austria, 24-26 September, University of Klagenfurt.
 
Le data mining appliqué aux bases de données de police: Que peut-on en attendre?
Terrettaz-Zufferey A.L., Ribaux O., Ratle F., Esseiva P., Kanevski M., 2007. Journal Criminalistique Suisse, 3. Peer-reviewed.
 
Machine Learning Algorithms for Analysis and Modeling of Geospatial Data
Pozdnoukhov A., Kanevski M., 2007. dans Annual Conference of International Accociation for Mathematical Geology (IAMG 07), Beijing, China, 25-31 August.
 
Machine learning algorithms for topo-climatic data modelling
Foresti L., Pozdnoukhov A., Kanevski M., 2007. dans European Colloquium on Theoretical and Quantitative Geography, Montreux, Switzerland.
 
Mapping of Environmental Data Using Kernel-Based Methods
Kanevski M., Pozdnoukhov A., Timonin V., Maignan M., 2007. Revue Internationale de Géomatique, 17 pp. 309-331. Peer-reviewed.
 
Pattern detection in forensic case data using graph theory : Application to heroin cutting agents.
Terrettaz-Zufferey A. L., Ratle F., Ribaux O., Esseiva P., Kanevski M., 2007. Forensic Science International, 167 (2-3) pp. 242-246.
 
Prediction of Wind Power Density using Machine Learning Algorithms
Pozdnoukhov A., Kanevski M., Timonin V., 2007. dans Annual Conference of International Accociation for Mathematical Geology.
 
Robust Nonlinear Mapping of Soil Contamination Using Support Regression
Kanevski M., Pozdnoukhov A., Timonin V., Maignan M., 2007. dans Annual Conference of International Accociation for Mathematical Geology, Beijing, China, International Accociation for Mathematical Geosciences.
Socio-economic cluster detection with spatial scan statistics. Case study: services at intra-urban scale
Tuia Devis, Kaiser Christian, Da Cunha Antonio, Kanevski Mikhail, 2007. dans Geocomputation 2007, National University of Ireland, Maynooth, 3-5 September 2007.
 
Soil Types Classification and Pollution Mapping with Machine Learning Methods
Kanevski M., Pozdnoukhov A., Timonin V., Maignan M., 2007. Pedometrics. Peer-reviewed.
 
Space-time cluster detection in crime data with scan statistics
Ratle F., Terrettaz-Zufferey A.L., Kanevski M., Esseiva P., Ribaux O., 2007. dans Spatial Econometrics Association Conference, Spatial Econometrics Association.
 
Spatial resolution enhancement of ASTER images using bayesian data fusion
Tuia D, Fasbender D., Bogaert P., Kanevski M., 2007. Journal Photogrammetric Engineering & Remote Sensing. Peer-reviewed.
 
Topo-climatic data: Analysis, modelling and geovisualization
Foresti L., Pozdnoukhov A., Kanevski M., 2007. dans Spatial Statistics and GIS conference, Klagenfurt, Austria, 24-26 September, University of Klagenfurt.
 
Comprehensive multivariate analysis of indoor radon data in Switzerland
Tapia R., Kanevski M., Maignan M., Gruson M., 2006. pp. 228-238 dans Barnet I., Neznal M., Pacherova P. (eds.) Radon investigations in the Czech Republic XI and the 8th international workshop on the Geological Aspects of Radon Risk Mapping, Czech Geological Survey, Radon v.o.s., Joint Research Center IES REM Ispra.
 
Indoor radon data monitoring networks: Topology, fractality and validity domains
Tuia D., Kanevski M., 2006. dans International Association for Mathematical Geology XIth International Congress, Liège, Belgium, IAMG.
 
Indoor radon risk mapping using geostatistical simulations
Kanevski M., Maignan M., Tapia R., 2006. dans Pirard E., Dassargues A., Havenish H.B. (eds.) XI International Congress for Mathematical Geology, Quantitative Geology from Multiple Sources, Liège, Belgium, International Association for Mathematical Geology.
 
Monitoring network optimisation for spatial data classification using support vector machines
Pozdnoukhov A., Kanevski M., 2006. International Journal of Environment and Pollution, 28 pp. 465-484. Peer-reviewed.
 
Scientific basis for indoor radon atlas
Kanevski M., Maignan M., Tapia R., Timonin V., Piller G., Gruson M., 2006. pp. 131-138 dans Barnet I., Neznal M., Pacherova P. (eds.) Radon investigations in the Czech Republic XI and the 8th international workshop on the Geological Aspects of Radon Risk Mapping, Joint Research Center IES REM Ispra.
 
Active Learning of Environmental Data using Support Vector Machines
Bevilacqua P., Lazzarini M., Kanevski M., 2005. dans 9th International conference of IAMG conference, Ischia, Italy.
 
BME-based uncertainty assessment of the Chernobyl fallout
Savelieva E., Demyanov V., Kanevski M., Serre M., Christakos G., 2005. Geoderma, 128 pp. 312-324. Peer-reviewed.
 
Contemporary Trends in Environmental Spatio-Temporal Data Analysis
Kanevski M., Savelieva E., Timonin V., Pozdnukhov A., 2005. dans European conference on environmental modelling, Russia.
 
Environmental data monitoring and modelling using gis and remote sensing technologies. Case study: Aral sea region
Tonini M., Tapia R., Hamel F., Kanevski M., 2005. pp. 313-314 dans 3rd Swiss Geoscience Meeting, Zürich, Switzerland.
 
Statistical learning theory for geospatial data. Case study: Aral sea
Kanevski M., Pozdnukhov A., Tonini M., Maignan M., Motelica M., Savelieva E., 2005. pp. 161-162 dans European colloquium on Theoretical and Quantitative Geography, Tomar, Portugal.
 
Statistical methods for river runoff prediction
Pisarenko V.F., Lyubushin A.A., Bolgov M.V., Rukavishnikova T.A., Kanyu S., Kanevski M., Saveleva E.A., Demyanov V., Zalyapin I.V., 2005. Water Resources, 32 pp. 115-126. Peer-reviewed.
 
Analysis and Modelling of Spatial Environmental Data
Kanevski M., Maignan M., 2004. 304, EPFL Press.
 
Environmental data mining and modeling based on machine learning algorithms and geostatistics
Kanevski M., Parkin R., Pozdnukhov A., Timonin V., Maignan M., Demyanov V., Canu S., 2004. Environmental Modelling and Software, 19 pp. 845-855. Peer-reviewed.
 
Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions
Kanevski M., Demyanov V., Pozdnukhov A., Parkin R., Savelieva E., Timonin V., Maignan M., 2003. Environmental Science and Pollution Research, SI pp. 137-149. Peer-reviewed.
 
Advanced Spatial Data Analysis and Modelling with Support Vector Machines
Kanevski M., Pozdnoukhov A., Canu S., Maignan M., 2002. International Journal of Fuzzy Systems, 4 pp. 606-616. Peer-reviewed.
 
Conditional Gaussian mixture models for environmental risk mapping
Gilardi N., Bengio S., Kanevski M., 2002. pp. 777 - 786 dans Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland. Peer-reviewed, IEEE Conference Publications.
 
Wavelet analysis residual kriging vs. neural network residual kriging
Demyanov V., Soltani S., Kanevski M., Canu S., Maignan M., Savelieva E., Timonin V., Pisarenko V., 2001. Stochastic Environmental Research and Risk Assessment, 15 pp. 18-32. Peer-reviewed.
 
BME analysis of neural network residual data from the Chernobyl fallout: bayesian and non-bayesian approaches
Christakos G., Serre M., Demyanov V., Timonin V., Kanevski M., Savelieva E., Chernov S., 2000. pp. 509-510 dans Monestiez P., Allard D., Froidevaux R. (eds.) geoENV III: Geostatistics for Environmental Applications Proceedings of the 3rd European Conference on Geostatistics for Environmental Applications, Avignon, France. Peer-reviewed.
 
Environmental Data Mapping with Support Vector Regression and Geostatistics
Kanevski M., Wong P.M., Canu S., 2000. 8.
 
Combining neural networks with kriging for stochastic reservoir modeling
Wang L., Wong P.M., Kanevski M., Gedeon T.D., 1999. In Situ, 23 pp. 151-169. Peer-reviewed.
 
Environmental and pollution spatial data classification with support vector machines and geostatistics
Gilardi N., Kanevski M., Maignan M., Mayoraz E., 1999. pp. 43-51 dans Intelligent techniques for Spatio-Temporal Data Analysis in Environmental Applications.
 
Mapping of radioactively Contaminated Territories with Geostatistics and Artificial Neural Networks
Kanevski M., Arutyunyan R., Bolshov L., Demyanov V., Savelieva E., Timonin V., Maignan M., Maignan M. F., 1999. Contaminated Forests, 58 pp. 249-256. Peer-reviewed.
 
Conditional stochastic cosimulations of the Chernobyl fallout
Kanevski M., Demyanov V., Savelieva E., Chernov S., Maignan M., 1998. dans Gomez-Hernandez J., Soares A.O., Froidevaux R. (eds.) GeoENV II - Geostatistics for Environmental Application, Kluwer Academic Publishers.
 
Environmental spatial data analysis with Geostat Office Software.
Kanevski M., Demyanov V., Chernov S., Savelieva E., Timonin V., Maignan M., 1998. pp. 161-166 dans Buccianti A. Nardi G. et Potenza R. (eds.) Proceeding if the Fourth Annual Conference of the International Association for Mathematical Geology, Napoli.
 
Geostat office for environmental and pollution spatial data analysis
Kanevski M., Demyanov V., Chernov S., Savelieva E., Serov A., Timonin V., Maignan M., 1998. Mathematische Geologie, 3 pp. 1-12. Peer-reviewed.
 
Neural Network Residual Kriging Application for Climatic Data
Demyanov V., Kanevski M., Chernov S., Savelieva E., Timonin V., 1998. Journal of Geographic Information and Decision Analysis, 2 pp. 215-232. Peer-reviewed.
 
Chernobyl Fallout: Review of Advanced Spatial Data Analysis
Kanevski M., Arutyunyan R., Bolshov L., Chernov S., Demyanov V., Koptelova N., Linge I., Savelieva E., Haas T., Maignan M., 1997. pp. 389-400 dans Soares A., Gomez-Hernandez J., Froidevaux R. (eds.) GeoENV I - Geostatistics for Environmental Application, Kluwer Academic Publishers.
 
Development of an information modeling system for Aral sea coastal region
Kiselev V.P., Arutunyan R.V., Le Coustumer P., Collet C., Kanevski M., Kuksenko V.F., Maignan M., Semin N.N., 1997. dans Ecosystems and Sustainable Development. Peer-reviewed, Wessex Institute of Technology.
 
Environmental decision-oriented mapping with algorithms imitating nature
Kanevski M., Maignan M., Demyanov V., Maignan M.F., 1997. pp. 520-526 dans Pavlovsky-Glahn V. (eds.) Proceedings of the Third Annual Conference of the International Association for Mathematical Geology. Peer-reviewed, Cimne.
 
Geostatistical portrayal of the Chernobyl accident
Kanevski M., Arutyunyan R., Bolshov L., Demyanov V., Linge I., Savelieva E., Shershakov V., Haas T., Maignan M., 1997. Quantitative Geology and Geostatistics, 8 pp. 1043-1054. Peer-reviewed.
 
How neural network 2-D interpolations can improve spatial data analysis: neural network residual kriging (NNRK)
Kanevski M., Maignan M., Demyanov V., Maignan M.F., 1997. pp. 5549-5554 dans Pavlovsky-Glahn V. (eds.) Proceedings of the Third Annual Conference of the International Association for Mathematical Geology. Peer-reviewed, Cimne.
 
Incremental neural networks for function approximation
Chentouf R., Jutten C., Maignan M., Kanevski M., 1997. Nuclear Instruments and Methods in Physics Research Section A - Accelerators, Spectrometers, Detectors, and Associated Equipment, 389 pp. 268-270. Peer-reviewed.
 
Multilayer perceptron with local constraint as an emerging method in spatial data analysis
De Bollivier M., Dubois G., Maignan M., Kanevski M., 1997. Nuclear Instruments and Methods in Physics Research Section A - Accelerators, Spectrometers, Detectors, and Associated Equipment, 389 pp. 226-229. Peer-reviewed.
 
Spatial estimations and and simulations of environmental data by using geostatistics and artificial neural networks
Kanevski M., Demyanov V., Maignan M., 1997. pp. 527-532 dans Pavlovsky-Glahn V. (eds.) Proceedings of the Third Annual Conference of the International Association for Mathematical Geology. Peer-reviewed, Cimne.
 
Artificial Neural Networks and Geostatistics for Environmental Mapping.
Kanevski M., Haas T., Maignan M et al. , 1996. dans Artificial Intelligence in Engineering.
 
Artificial neural networks and spatial estimation of Chernobyl fallout
Kanevski M., Arutyunyan R., Bolshov L., Demyanov V., Maignan M., 1996. Geoinformatics, 7 pp. 5-11. Peer-reviewed.
 
Environmental decision support system on base of geoinformational technologies for the analysis of nuclear accident consequences
Arutyunyan R.V., Bolshov L.A., Demianov V.V., Glushko A.V., Kabalevski S.A., Kanevski M., Kiselev V.P., Koptelova N.A., Krylov S.F., Linge I.I. et al., 1996. pp. 539-542 dans The radiological consequences of the Chernobyl accident.
 
Neural network 2-D interpolations for spatial data analysis: Neural network residual kriging (NNRK)
Maignan M., Kanevski M., Demianov V., 1996. dans Canu S. (eds.) Artificial Intelligence in Engineering AIENG'96.
 
Neural network time-series analysis and prediction for radioactivity monitoring
Kanevski M., Maignan M., Kuksenko V., 1996. dans Canu S. (eds.) Proceedings od the AIHENP'96.
 
Neural networks and other flexible regression estimators for spatial interpolation
Soltani S., Maignan M., Kanevski M., 1996. dans Canu S. (eds.) Artificial Intelligence in Engineering AIENG'96.
 
Soil Pollution: environmental integrated systems and prediction mapping
Kanevski M., Arutyunyan R., Bolshov L., Demyanov V., Kabalevskiy S., Kanevskaya E., Kiselev V., Koptelova N., Linge I., Martynenko E. et al., 1996. pp. 47-56 dans Development and Application of Computer Techniques to Environmental Studies, Computational Mechanics Publications.
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