J. Space Weather Space Clim.
Volume 5, 2015
Statistical Challenges in Solar Information Processing
Article Number A23
Number of page(s) 12
Published online 10 July 2015
  • Ahammer, H., J.M. Kröpfl, C. Hackl, and R. Sedivy. Image statistics and data mining of anal intraepithelial neoplasia. Pattern Recognit. Lett., 29 (16), 2189–2196, 2008, DOI: 10.1016/j.patrec.2008.08.008. [CrossRef] [Google Scholar]
  • Anscombe, F.J. The transformation of Poisson, binomial and negative-binomial data. Biometrika, 35, 246–254, 1948. [CrossRef] [Google Scholar]
  • Barra, V., V. Delouille, and J.F. Hochedez. Segmentation of extreme ultraviolet solar images using a multispectral data fusion process. In Fuzzy Systems Conference, 2007. FUZZ-IEEE 2007, IEEE International, London, 1–6, 2007, DOI: 10.1109/FUZZY.2007.4295367. [Google Scholar]
  • Bezdek, J.C., Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981. [CrossRef] [Google Scholar]
  • Bloomfield, D.S., P.A. Higgins, R.T.J. McAteer, and P.T. Gallagher. Toward reliable benchmarking of solar flare forecasting methods. ApJ, 747, L41, 2012, DOI: 10.1088/2041-8205/747/2/L41. [Google Scholar]
  • Breiman, L. Arcing classifier. Ann. Statist., 26 (3), 801–849, 1998, DOI: 10.1214/aos/1024691079. [CrossRef] [MathSciNet] [Google Scholar]
  • Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees, Chapman & Hall, New York, 1984. [Google Scholar]
  • Chang, C.-C., and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 27:1–27:27, 2011. Software available at [Google Scholar]
  • Cranmer, S.R. Coronal Holes. Living Rev. Sol. Phys., 6, 3, 2009, DOI: 10.12942/lrsp-2009-3. [Google Scholar]
  • de Toma, G. Evolution of coronal holes and implications for high-speed solar wind during the minimum between cycles 23 and 24. Sol. Phys., 274, 195–217, 2011, DOI: 10.1007/s11207-010-9677-2. [NASA ADS] [CrossRef] [Google Scholar]
  • Delouille, V., P. Chainais, and J.-F. Hochedez. Spatial and temporal noise in solar EUV observations. Sol. Phys., 248, 441–455, 2008, DOI: 10.1007/s11207-008-9131-x. [CrossRef] [Google Scholar]
  • Fan, R.-E., K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research, 9, 1871–1874, 2008. [Google Scholar]
  • Gosling, J.T., and V.J. Pizzo. Formation and evolution of corotating interaction regions and their three dimensional structure. Space Sci. Rev., 89, 21–52, 1999, DOI: 10.1023/A:1005291711900. [Google Scholar]
  • Hanssen, A., and W. Kuipers. On the Relationship Between the Frequency of Rain and Various Meteorological Parameters: (with Reference to the Problem Of Objective Forecasting). In: Koninkl. Nederlands Meterologisch Institut. Mededelingen en Verhandelingen, 's-Gravenhage: Staatsdrukkerij- en Uitgeverijbedrijf, 1965. [Google Scholar]
  • Haralick, R.M., K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3 (6), 610–621, 1973, DOI: 10.1109/tsmc.1973.4309314. [Google Scholar]
  • Hurlburt, N., M. Cheung, C. Schrijver, L. Chang, S. Freeland, et al. Heliophysics event knowledgebase for the solar dynamics observatory (SDO) and beyond. Sol. Phys., 275, 67–78, 2012, DOI: 10.1007/s11207-010-9624-2. [Google Scholar]
  • Japkowics, N., and M. Shah. Evaluating Learning Algorithms: A Classification Perspective, Cambridge University Press, 2014. [Google Scholar]
  • Kirk, M.S., W.D. Pesnell, C.A. Young, and S.A. Hess Weber. Automated detection of EUV polar coronal holes during solar cycle 23. Sol. Phys., 257, 99–112, 2009, DOI: 10.1007/s11207-009-9369-y. [Google Scholar]
  • Krista, L.D., and P.T. Gallagher. Automated coronal hole detection using local intensity thresholding techniques. Sol. Phys., 256, 87–100, 2009, DOI: 10.1007/s11207-009-9357-2. [Google Scholar]
  • Lemen, J.R., A.M. Title, D.J. Akin, P.F. Boerner, C. Chou, et al. The atmospheric imaging assembly (AIA) on the solar dynamics observatory (SDO). Sol. Phys., 275, 17–40, 2012, DOI: 10.1007/s11207-011-9776-8. [NASA ADS] [CrossRef] [Google Scholar]
  • Mackay, D.H., J.T. Karpen, J.L. Ballester, B. Schmieder, and G. Aulanier. Physics of solar prominences: II – Magnetic structure and dynamics. Space Sci. Rev., 151, 333–399, 2010, DOI: 10.1007/s11214-010-9628-0. [NASA ADS] [CrossRef] [Google Scholar]
  • Martens, P.C.H., G.D.R. Attrill, A.R. Davey, A. Engell, S. Farid, et al. Computer vision for the solar dynamics observatory (SDO). Sol. Phys., 275, 79–113, 2012, DOI: 10.1007/s11207-010-9697-y. [Google Scholar]
  • Müller, D., B. Fleck, G. Dimitoglou, B.W. Caplins, D.E. Amadigwe, et al. JHelioviewer: visualizing large sets of solar images using JPEG 2000. Computing in Science and Engineering, 11 (5), 38–47, 2009. [Google Scholar]
  • Munro, R.H., and G.L. Withbroe. Properties of a coronal “hole” derived from extreme-ultraviolet observations. ApJ, 176, 511, 1972, DOI: 10.1086/151653. [NASA ADS] [CrossRef] [Google Scholar]
  • Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830, 2011. [Google Scholar]
  • Pötzi, W., A.M. Veronig, G. Riegler, U. Amerstorfer, T. Pock, M. Temmer, W. Polanec, and D. Baumgartner. Real-time flare detection in ground-based Hα imaging at Kanzelhohe observatory. Sol. Phys., 290 (3), 951–977, 2015, DOI: 10.1007/s11207-014-0640-5. [Google Scholar]
  • Quinlan, J.R. C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, ISBN 1-55860-238-0, 1993. [Google Scholar]
  • Reiss, M.A., M. Temmer, T. Rotter, S.J. Hofmeister, and A.M. Veronig. Identification of coronal holes and filament channels in SDO/AIA 193Å images via geometrical classification methods. Central European Astrophysical Bulletin, 38, 95–104, 2014. [Google Scholar]
  • Rotter, T., A.M. Veronig, M. Temmer, and B. Vršnak. Relation between coronal hole areas on the sun and the solar wind parameters at 1 AU. Sol. Phys., 281, 793–813, 2012, DOI: 10.1007/s11207-012-0101-y. [NASA ADS] [CrossRef] [Google Scholar]
  • Rotter, T., A.M. Veronig, M. Temmer, and B. Vršnak. Real-time solar wind prediction based on SDO/AIA coronal hole data. Sol. Phys., 290 (5), 1355–1370, 2015, DOI: 10.1007/s11207-015-0680-5. [Google Scholar]
  • Scherrer, P.H., J. Schou, R.I. Bush, A.G. Kosovichev, R.S. Bogart, et al. The helioseismic and magnetic imager (HMI) investigation for the solar dynamics observatory (SDO). Sol. Phys., 275, 207–227, 2012, DOI: 10.1007/s11207-011-9834-2. [Google Scholar]
  • Tsurutani, B.T., W.D. Gonzalez, A.L.C. Gonzalez, F.L. Guarnieri, N. Gopalswamy, et al. Corotating solar wind streams and recurrent geomagnetic activity: a review. J. Geophys. Res. [Space Phys.], 111, A07S01, 2006, DOI: 10.1029/2005JA011273. [Google Scholar]
  • Vapnick, V. Statistical Learning Theory, Wiley, New York, 1998. [Google Scholar]
  • Verbanac, G., B. Vršnak, A.M. Veronig, and M. Temmer. Equatorial coronal holes, solar wind high-speed streams, and their geoeffectiveness. A&A, 526, A20, 2011, DOI: 10.1051/0004-6361/201014617. [Google Scholar]
  • Verbeeck, C., V. Delouille, B. Mampaey, and R. De Visscher. The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images. A&A, 561, A29, 2014, DOI: 10.1051/0004-6361/201321243. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  • Vršnak, B., M. Temmer, and A.M. Veronig. Coronal holes and solar wind high-speed streams: I. Forecasting the solar wind parameters. Sol. Phys., 240, 315–330, 2007, DOI: 10.1007/s11207-007-0285-8. [Google Scholar]
  • Weyn, B., W. Tjalam, G.V. de Wouwer, A.V. Daele, P. Scheunders, W. Jacob, E.V. Marck, and D.V. Dyck. Validation of nuclear texture density, morphometry and tissue syntactic structure analysis as prognosticators of cervical carcinoma. Analytical and Quantitative Cytology and Histology, 22 (5), 373–382, 2000. [Google Scholar]
  • Woodcock, F. The evaluation of yes/no forecasts for scientific and administrative purposes. Monthly Weather Review, 104, 1209, 1976, DOI: 10.1175/1520-0493(1976)104<1209:TE0YFF>2.0.C0;2. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.