The Supervised Normalized Cut Method for Detecting, Classifying, and Identifying Special Nuclear Materials

Published Online:https://doi.org/10.1287/ijoc.1120.0546

References

  • Bertozzi W, Ledoux RJ (2005) Nuclear resonance fluorescence imaging in non-intrusive cargo inspection. Nuclear Instruments and Methods Phys. Res. Sect. B: Beam Interactions with Materials and Atoms 241(1–4):820–825.CrossrefGoogle Scholar
  • Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2):121–167.CrossrefGoogle Scholar
  • Carneiro G, Chan AB, Moreno PJ, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Machine Intelligence 29:394–410.CrossrefGoogle Scholar
  • Carpenter T, Cheng J, Roberts F, Xie M (2010) Sensor management problems of nuclear detection. Pham H, ed. Safety and Risk Modeling and Its Applications (Springer, London), 299–323.Google Scholar
  • Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. Cohen W, Moore A, eds. Proc. 23rd Internat. Conf. Machine Learn., ICML '06 (ACM, New York), 161–168.CrossrefGoogle Scholar
  • Chang C-C, Lin C-J (2001) LIBSVM: A Library For Support Vector Machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.Google Scholar
  • Cox IJ, Rao SB, Zhong Y (1996) Ratio regions: A technique for image segmentation. Kropatsch WG, ed. Proc. Int. Conf. Pattern Recognition, Vol. B (IEEE Computer Society Press, Los Alamitos, CA), 557–564.CrossrefGoogle Scholar
  • Crammer K, Singer Y (2002) On the learnability and design of output codes for multiclass problems. Machine Learn. 47(2–3):201–233.CrossrefGoogle Scholar
  • Cristianni N, Shawe-Taylor J (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Ding YS, Zhang TL, Chou KC (2007) Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Peptide Lett. 14(8):811–815.CrossrefGoogle Scholar
  • Dowd PA, Pardo-Iguzquiza E (2005) Estimating the boundary surface between geologic formations from 3D seismic data using neural networks and geostatistics. Geophysics 70(1):1–11.CrossrefGoogle Scholar
  • Duan K-B, Keerthi SS (2005) Which is the best multiclass SVM method? An empirical study. Oza NC, Robi P, Josef K, Fabio R, eds. Multiple Classifier Systems, Lecture Notes in Computer Science, Vol. 3541 (Springer, Berlin), 732–760.CrossrefGoogle Scholar
  • Duda RO, Hart PE, Stork DG (2001) Unsupervised learning and clustering. Pattern Classification, Chap. 10, 2nd ed. (Wiley, New York).Google Scholar
  • Ford LR, Fulkerson DR (1956) Maximal flow through a network. Canadian J. Math. 8(3):399–404.CrossrefGoogle Scholar
  • Fung G, Mangasarian OL (2002) A feature selection Newton method for support vector machine classification. Technical report, University of Wisconsin, Madison.Google Scholar
  • Fung G, Mangasarian OL (2004) A feature selection Newton method for support vector machine classification. Comput. Optim. Appl. 28(2):185–202.CrossrefGoogle Scholar
  • Fung GM, Mangasarian OL (2005) Multicategory proximal support vector machine classifiers. Machine Learn. 59(1–2):77–97.CrossrefGoogle Scholar
  • Geisinger NP (2010) Classification of digital modulation schemes using linear and nonlinear classifiers. Ph.D. thesis, Naval Postgraduate School, Monterey, CA.Google Scholar
  • Gentile CA (2010) US Patent 7,711,661 b2, filed May 2, 2007, issued May 4, 2010.Google Scholar
  • Goldschmidt O, Hochbaum DS (1994) A polynomial algorithm for the k-cut problem for fixed k. Math. Oper. Res. 19(1):24–37.LinkGoogle Scholar
  • Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Machine Learn. 46(1–3):389–422.CrossrefGoogle Scholar
  • Hastie T, Rosset S, Tibshirani R, Zhu J (2004) The entire regularization path for the support vector machine. J. Machine Learn. Res. 5(1):1391–1415.Google Scholar
  • Hochbaum DS (2010a) HPF: Hochbaum's Pseudo-Flow Algorithm Implementation. Software available at http://riot.ieor.berkeley.edu/riot/Applications/Pseudoflow/maxflow.html.Google Scholar
  • Hochbaum DS (2010b) Polynomial time algorithms for ratio regions and a variant of normalized cut. IEEE Trans. Pattern Anal. Machine Intelligence 32(5):889–898.CrossrefGoogle Scholar
  • Hua Z, Wang Y, Xu X, Zhang B, Liang L (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems Appl. 33(2): 434–440.CrossrefGoogle Scholar
  • International Atomic Energy Agency (2007) Combating illicit trafficking in nuclear and other radioactive material. Technical report/reference manual, IAEA Nuclear Security Series 6, International Atomic Energy Agency, Vienna, Austria.Google Scholar
  • Kangas LJ, Keller PE, Siciliano ER, Kouzes RT, Ely JH (2008) The use of artificial neural networks in PVT-based radiation portal monitors. Nuclear Instruments Methods Phys. Res. Sect. A, Accelerators, Spectrometers, Detectors Associated Equipment 587(2–3): 398–412.CrossrefGoogle Scholar
  • Kelly EL (1962) General description and operating characteristics of the Berkeley 88-inch cyclotron. Nuclear Instruments Methods 18–19(1):33–40.CrossrefGoogle Scholar
  • Kotsiantis S (2007) Supervised learning: A review of classification techniques. Informatica 31(1):249–268.Google Scholar
  • Luo L (2006) Chemometrics and its applications to X-ray spectrometry. X-ray Spectrometry 35(4):215–225.CrossrefGoogle Scholar
  • Mangasarian OL, Wild EW (2007) Nonlinear knowledge in kernel approximation. IEEE Trans. Neural Networks 18(1):300–306.CrossrefGoogle Scholar
  • Mangasarian OL, Wild EW (2008) Nonlinear knowledge-based classification. IEEE Trans. Neural Networks 19(10):1826–1832.CrossrefGoogle Scholar
  • Marrs RE, Norman EB, Burke JT, Macri RA, Shugart HA, Browne E, Smith AR (2008) Fission-product gamma-ray line pairs sensitive to fissile material and neutron energy. Nuclear Instruments Methods Phys. Res. Sect. A: Accelerators, Spectrometers, Detectors Associated Equipment 592(3):463–471.CrossrefGoogle Scholar
  • Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans. Pattern Anal. Machine Intelligence 23(2):228–233.CrossrefGoogle Scholar
  • McLachlan G (2004) Discriminant Analysis and Statistical Pattern Recognition (Wiley Interscience, New York).Google Scholar
  • Mihailescu L, Fishbain B, Hochbaum DS, Maltz J, Moore CJ, Rohel J, Supic L, Vetter K (2010) Dynamic stand-off 3D gamma-ray imaging. Wehe DK, ed. 12th Sympos. Radiation Measurements Appl. (SORMA) (National Nuclear Security Administration, Washington, DC), 106.Google Scholar
  • Norman EB, Prussin SG, Larimer R-M, Shugart H, Browne E, Smith AR, McDonald RJet al. (2004) Signatures of fissile materials: High-energy [gamma] rays following fission. Nuclear Instruments Methods Phys. Res. Sect. A: Accelerators, Spectrometers, Detectors Associated Equipment 521(2–3):608–610.CrossrefGoogle Scholar
  • Resson HW, Varghese RS, Goldman R (2009) Computational methods for analysis of MALDI-TOF spectra to discover peptide serum biomarkers. The Protein Protocols Handbook IV:1175–1183.CrossrefGoogle Scholar
  • Rifkin R, Klautau A (2004) In defense of one-vs-all classication. J. Machine Learn. Res. 5(1):101–141.Google Scholar
  • Sharon E, Galun M, Sharon D, Basri R, Brandt A (2006) Hierarchy and adaptivity in segmenting visual scenes. Nature 442(7104): 810–813.CrossrefGoogle Scholar
  • Shi J, Malik J (2000) Normalized cut and image segmentation. IEEE Trans. Pattern Anal. Machine Intelligence 22(8):888–905.CrossrefGoogle Scholar
  • Swanberg E, Norman E, Shugart H, Prussin S, Browne E (2009) Using low resolution gamma detectors to detect and differentiate 239Pu and 235U fissions. J. Radioanalytical Nuclear Chemistry 282(3):901–904.CrossrefGoogle Scholar
  • Wang F, Qian Y, Dai Y, Wang Z (2010) A model based on hybrid support vector machine and self-organizing map for anomaly detection. Wang C-X, Fan P, Shen X, He Y, eds. Comm. Mobile Comput., Internat. Conf. Vol. 1 (IEEE Computer Society, Los Alamitos, CA), 97–101.CrossrefGoogle Scholar
  • West D (2000) Neural network credit scoring models. Comput. Oper. Res. 27(11–12):1131–1152.CrossrefGoogle Scholar
  • Zhang J, Zhang XD, Ha S-W (2008) A novel approach using PCA and SVM for face detection. Guo M, Zhao L, Wang L, eds. Fourth Internat. Conf. Nat. Comput., (ICNC '08) Vol. 3 (IEEE Computer Society, Los Alamitos, CA), 29–33.CrossrefGoogle Scholar
  • Zhu J, Rosset S, Hastie T, Tibshirani R (2003) 1-norm support vector machines. Thrun S, Saul L, Schölkopf B, eds. Neural Information Processing Systems (MIT Press, Cambridge, MA), 16–23.Google Scholar
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