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kernlab - An S4 Package for Kernel Methods in R

Citations

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Cited by:

  1. Yli-Heikkilä, Maria & Tauriainen, Jukka, 2014. "Profitability prediction model for dairy farms using the random forest method," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182846, European Association of Agricultural Economists.
  2. Demers Simon, 2015. "Riding a probabilistic support vector machine to the Stanley Cup," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 11(4), pages 205-218, December.
  3. Grabisch, Michel & Kojadinovic, Ivan & Meyer, Patrick, 2008. "A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: Applications of the Kappalab R package," European Journal of Operational Research, Elsevier, vol. 186(2), pages 766-785, April.
  4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
  5. Pimenta, Mayra & Andrade, André Felipe Alves de & Fernandes, Fernando Hiago Souza & Amboni, Mayra Pereira de Melo & Almeida, Renata Silva & Soares, Ana Hermínia Simões de Bello & Falcon, Guth Berger &, 2022. "One size does not fit all: Priority areas for real world problems," Ecological Modelling, Elsevier, vol. 470(C).
  6. Tsukioka, Yasutomo & Yanagi, Junya & Takada, Teruko, 2018. "Investor sentiment extracted from internet stock message boards and IPO puzzles," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 205-217.
  7. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
  8. Bommert, Andrea & Sun, Xudong & Bischl, Bernd & Rahnenführer, Jörg & Lang, Michel, 2020. "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  9. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
  10. repec:hum:wpaper:sfb649dp2012-030 is not listed on IDEAS
  11. M. Ballings & D. Van Den Poel, 2012. "Kernel Factory: An Ensemble of Kernel Machines," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/825, Ghent University, Faculty of Economics and Business Administration.
  12. Daniel J. Luckett & Eric B. Laber & Samer S. El‐Kamary & Cheng Fan & Ravi Jhaveri & Charles M. Perou & Fatma M. Shebl & Michael R. Kosorok, 2021. "Receiver operating characteristic curves and confidence bands for support vector machines," Biometrics, The International Biometric Society, vol. 77(4), pages 1422-1430, December.
  13. Zeyu Tang & Jinzhu Jia, 2022. "The Association between the Burden of PM 2.5 -Related Neonatal Preterm Birth and Socio-Demographic Index from 1990 to 2019: A Global Burden Study," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
  14. Gallego-Castillo, Cristobal & Bessa, Ricardo & Cavalcante, Laura & Lopez-Garcia, Oscar, 2016. "On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power," Energy, Elsevier, vol. 113(C), pages 355-365.
  15. Senait D Senay & Susan P Worner & Takayoshi Ikeda, 2013. "Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-16, August.
  16. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2011. "Synth: An R Package for Synthetic Control Methods in Comparative Case Studies," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i13).
  17. Minjae Park & Mi Lim Lee & Jinpyo Lee, 2019. "Predicting Stock Market Indices Using Classification Tools," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 9(2), pages 243-256, February.
  18. Hermel Homburger & Manuel K Schneider & Sandra Hilfiker & Andreas Lüscher, 2014. "Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
  19. Keach Murakami & Seiji Shimoda & Yasuhiro Kominami & Manabu Nemoto & Satoshi Inoue, 2021. "Prediction of municipality-level winter wheat yield based on meteorological data using machine learning in Hokkaido, Japan," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-19, October.
  20. Shusaku Tsumoto & Tomohirno Kimura & Shoji Hirano, 2021. "Determination of Disease from Discharge Summaries," The Review of Socionetwork Strategies, Springer, vol. 15(1), pages 49-66, June.
  21. Uwe Ligges & Sebastian Krey, 2011. "Feature clustering for instrument classification," Computational Statistics, Springer, vol. 26(2), pages 279-291, June.
  22. Cannon, Alex J., 2017. "Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes," Earth Arxiv wg7sn, Center for Open Science.
  23. P. Vangay & J. Steingrimsson & M. Wiedmann & M. J. Stasiewicz, 2014. "Classification of Listeria monocytogenes Persistence in Retail Delicatessen Environments Using Expert Elicitation and Machine Learning," Risk Analysis, John Wiley & Sons, vol. 34(10), pages 1830-1845, October.
  24. Koenker, Roger & Mizera, Ivan, 2014. "Convex Optimization in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 60(i05).
  25. repec:jss:jstsof:15:i09 is not listed on IDEAS
  26. Georgia Papacharalampous & Hristos Tyralis & Demetris Koutsoyiannis, 2018. "Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5207-5239, December.
  27. Härdle, Wolfgang Karl & Prastyo, Dedy Dwi & Hafner, Christian, 2012. "Support vector machines with evolutionary feature selection for default prediction," SFB 649 Discussion Papers 2012-030, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  28. Babak Fazelabdolabadi, 2019. "A hybrid Bayesian-network proposition for forecasting the crude oil price," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-21, December.
  29. Arnout Van Messem & Andreas Christmann, 2010. "A review on consistency and robustness properties of support vector machines for heavy-tailed distributions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(2), pages 199-220, September.
  30. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
  31. Benedetto Grillone & Gerard Mor & Stoyan Danov & Jordi Cipriano & Florencia Lazzari & Andreas Sumper, 2021. "Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology," Energies, MDPI, vol. 14(17), pages 1-30, September.
  32. Riza, Lala Septem & Bergmeir, Christoph & Herrera, Francisco & Benítez, José M., 2015. "frbs: Fuzzy Rule-Based Systems for Classification and Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 65(i06).
  33. Jacobi Liana & Kwok Chun Fung & Ramírez-Hassan Andrés & Nghiem Nhung, 2024. "Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 403-434, April.
  34. Mendes, Poliana & Velazco, Santiago José Elías & Andrade, André Felipe Alves de & De Marco, Paulo, 2020. "Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy," Ecological Modelling, Elsevier, vol. 431(C).
  35. Karin Wolffhechel & Amanda C Hahn & Hanne Jarmer & Claire I Fisher & Benedict C Jones & Lisa M DeBruine, 2015. "Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
  36. Guanhua Chen & Donglin Zeng & Michael R. Kosorok, 2016. "Personalized Dose Finding Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1509-1521, October.
  37. Tobias Rentschler & Philipp Gries & Thorsten Behrens & Helge Bruelheide & Peter Kühn & Steffen Seitz & Xuezheng Shi & Stefan Trogisch & Thomas Scholten & Karsten Schmidt, 2019. "Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-23, August.
  38. Takahiro Takamatsu & Hideaki Ohtake & Takashi Oozeki, 2022. "Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation," Energies, MDPI, vol. 15(4), pages 1-18, February.
  39. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
  40. Parreira, Micael Rosa & Nabout, João Carlos & Tessarolo, Geiziane & de Souza Lima-Ribeiro, Matheus & Teresa, Fabrício Barreto, 2019. "Disentangling uncertainties from niche modeling in freshwater ecosystems," Ecological Modelling, Elsevier, vol. 391(C), pages 1-8.
  41. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
  42. Tomasz Hachaj & Marek R. Ogiela & Katarzyna Koptyra, 2018. "Human actions recognition from motion capture recordings using signal resampling and pattern recognition methods," Annals of Operations Research, Springer, vol. 265(2), pages 223-239, June.
  43. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2020. "Company classification using machine learning," Papers 2004.01496, arXiv.org, revised May 2020.
  44. Cipollini, Francesca & Oneto, Luca & Coraddu, Andrea & Murphy, Alan John & Anguita, Davide, 2018. "Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 12-23.
  45. Rachel Sippy & Daniel F Farrell & Daniel A Lichtenstein & Ryan Nightingale & Megan A Harris & Joseph Toth & Paris Hantztidiamantis & Nicholas Usher & Cinthya Cueva Aponte & Julio Barzallo Aguilar & An, 2020. "Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 14(2), pages 1-20, February.
  46. Nunes, Matthew, 2015. "Statistical Analysis of Network Data with R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(b01).
  47. Jorge Daniel Mello-Román & Adolfo Hernández & Julio César Mello-Román, 2021. "Improved Predictive Ability of KPLS Regression with Memetic Algorithms," Mathematics, MDPI, vol. 9(5), pages 1-13, March.
  48. Christopher Gandrud & Mark Hallerberg, 2015. "What is a Financial Crisis? Efficiently Measuring Real-Time Perceptions of Financial Market Stress with an Application to Financial Crisis Budget Cycles," CESifo Working Paper Series 5632, CESifo.
  49. Huisheng Wu & Maogui Hu & Yaping Zhang & Yuan Han, 2021. "An Empirical Mode Decomposition for Establishing Spatiotemporal Air Quality Trends in Shandong Province, China," Sustainability, MDPI, vol. 13(22), pages 1-10, November.
  50. Becker, Martin & Klößner, Stefan, 2018. "Fast and reliable computation of generalized synthetic controls," Econometrics and Statistics, Elsevier, vol. 5(C), pages 1-19.
  51. Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
  52. Zeyu Tang & Jinzhu Jia, 2022. "PM2.5-Related Neonatal Infections: A Global Burden Study from 1990 to 2019," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
  53. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
  54. Matthew N Ahmadi & Alok Chowdhury & Toby Pavey & Stewart G Trost, 2020. "Laboratory-based and free-living algorithms for energy expenditure estimation in preschool children: A free-living evaluation," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-14, May.
  55. Maia, Mateus & Pimentel, Jonatha S. & Pereira, Ivalbert S. & Gondim, João & Barreto, Marcos E. & Ara, Anderson, 2020. "Convolutional support vector models: prediction of coronavirus disease using chest X-rays," LSE Research Online Documents on Economics 115769, London School of Economics and Political Science, LSE Library.
  56. Shaobo Jin & Sebastian Ankargren, 2019. "Frequentist Model Averaging in Structural Equation Modelling," Psychometrika, Springer;The Psychometric Society, vol. 84(1), pages 84-104, March.
  57. repec:jss:jstsof:42:i13 is not listed on IDEAS
  58. Lili Tan & Yunzhan Gong & Yawen Wang, 2019. "A Model for Predicting Statement Mutation Scores," Mathematics, MDPI, vol. 7(9), pages 1-39, August.
  59. Joseph Heffner & Oriel FeldmanHall, 2022. "A probabilistic map of emotional experiences during competitive social interactions," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  60. Islam Shofiqul & Anand Sonia & Hamid Jemila & Thabane Lehana & Beyene Joseph, 2017. "Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(3), pages 199-216, August.
  61. Ballings, Michel & Van den Poel, Dirk & Bogaert, Matthias, 2016. "Social media optimization: Identifying an optimal strategy for increasing network size on Facebook," Omega, Elsevier, vol. 59(PA), pages 15-25.
  62. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
  63. Anna Hake & Nico Pfeifer, 2017. "Prediction of HIV-1 sensitivity to broadly neutralizing antibodies shows a trend towards resistance over time," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-23, October.
  64. Maria-Carmen García-Centeno & Román Mínguez-Salido & Raúl del Pozo-Rubio, 2021. "The Classification of Profiles of Financial Catastrophe Caused by Out-of-Pocket Payments: A Methodological Approach," Mathematics, MDPI, vol. 9(11), pages 1-20, May.
  65. Claudio Conversano & Elise Dusseldorp, 2017. "Modeling Threshold Interaction Effects Through the Logistic Classification Trunk," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 399-426, October.
  66. Stephen J Gilmore, 2018. "Automated decision support in melanocytic lesion management," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
  67. Konrad Bogner & Florian Pappenberger & Massimiliano Zappa, 2019. "Machine Learning Techniques for Predicting the Energy Consumption/Production and Its Uncertainties Driven by Meteorological Observations and Forecasts," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
  68. De Brabanter, Kris & Suykens, Johan & De Moor, Bart, 2013. "Nonparametric Regression via StatLSSVM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i02).
  69. Andrea S Martinez-Vernon & James A Covington & Ramesh P Arasaradnam & Siavash Esfahani & Nicola O’Connell & Ioannis Kyrou & Richard S Savage, 2018. "An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
  70. Tyler C Shimko & Erik C Andersen, 2014. "COPASutils: An R Package for Reading, Processing, and Visualizing Data from COPAS Large-Particle Flow Cytometers," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-5, October.
  71. repec:jss:jstsof:28:i05 is not listed on IDEAS
  72. Zulj, Valentin & Jin, Shaobo, 2024. "Can model averaging improve propensity score based estimation of average treatment effects?," Working Paper Series 2024:1, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  73. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
  74. Yasset Perez-Riverol & Max Kuhn & Juan Antonio Vizcaíno & Marc-Phillip Hitz & Enrique Audain, 2017. "Accurate and fast feature selection workflow for high-dimensional omics data," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
  75. Karatzoglou, Alexandros & Feinerer, Ingo, 2010. "Kernel-based machine learning for fast text mining in R," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 290-297, February.
  76. Maria D. Gonzalez-Lima & Carenne C. Ludeña, 2022. "Using Locality-Sensitive Hashing for SVM Classification of Large Data Sets," Mathematics, MDPI, vol. 10(11), pages 1-21, May.
  77. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
  78. Khamma, Thulasi Ram & Zhang, Yuming & Guerrier, Stéphane & Boubekri, Mohamed, 2020. "Generalized additive models: An efficient method for short-term energy prediction in office buildings," Energy, Elsevier, vol. 213(C).
  79. Madhumita Sahoo & Aman Kasot & Anirban Dhar & Amlanjyoti Kar, 2018. "On Predictability of Groundwater Level in Shallow Wells Using Satellite Observations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1225-1244, March.
  80. Huber, Martin & Imhof, David, 2023. "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, vol. 89(C).
  81. Heungsun Hwang & Gyeongcheol Cho, 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 947-972, December.
  82. Sergio Picart-Armada & Steven J Barrett & David R Willé & Alexandre Perera-Lluna & Alex Gutteridge & Benoit H Dessailly, 2019. "Benchmarking network propagation methods for disease gene identification," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-24, September.
  83. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
  84. Jeffrey R. Stevens & Alexis Polzkill Saltzman & Tanner Rasmussen & Leen-Kiat Soh, 2021. "Improving measurements of similarity judgments with machine-learning algorithms," Journal of Computational Social Science, Springer, vol. 4(2), pages 613-629, November.
  85. P. J. Zarco-Tejada & T. Poblete & C. Camino & V. Gonzalez-Dugo & R. Calderon & A. Hornero & R. Hernandez-Clemente & M. Román-Écija & M. P. Velasco-Amo & B. B. Landa & P. S. A. Beck & M. Saponari & D. , 2021. "Divergent abiotic spectral pathways unravel pathogen stress signals across species," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
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