The Supervised Normalized Cut Method for Detecting, Classifying, and Identifying Special Nuclear Materials
Published Online:11 Apr 2013https://doi.org/10.1287/ijoc.1120.0546
References
- (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.Crossref, Google Scholar
- (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2):121–167.Crossref, Google Scholar
- (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Anal. Machine Intelligence 29:394–410.Crossref, Google Scholar
- (2010) Sensor management problems of nuclear detection. Pham H, ed. Safety and Risk Modeling and Its Applications (Springer, London), 299–323.Google Scholar
- (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.Crossref, Google Scholar
- (2001) LIBSVM: A Library For Support Vector Machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.Google Scholar
- (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.Crossref, Google Scholar
- (2002) On the learnability and design of output codes for multiclass problems. Machine Learn. 47(2–3):201–233.Crossref, Google Scholar
- (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2007) Prediction of protein structure classes with pseudo amino acid composition and fuzzy support vector machine network. Protein Peptide Lett. 14(8):811–815.Crossref, Google Scholar
- (2005) Estimating the boundary surface between geologic formations from 3D seismic data using neural networks and geostatistics. Geophysics 70(1):1–11.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2001) Unsupervised learning and clustering. Pattern Classification, Chap. 10, 2nd ed. (Wiley, New York).Google Scholar
- (1956) Maximal flow through a network. Canadian J. Math. 8(3):399–404.Crossref, Google Scholar
- (2002) A feature selection Newton method for support vector machine classification. Technical report, University of Wisconsin, Madison.Google Scholar
- (2004) A feature selection Newton method for support vector machine classification. Comput. Optim. Appl. 28(2):185–202.Crossref, Google Scholar
- (2005) Multicategory proximal support vector machine classifiers. Machine Learn. 59(1–2):77–97.Crossref, Google Scholar
- (2010) Classification of digital modulation schemes using linear and nonlinear classifiers. Ph.D. thesis, Naval Postgraduate School, Monterey, CA.Google Scholar
- (2010) US Patent 7,711,661 b2, filed May 2, 2007, issued May 4, 2010.Google Scholar
- (1994) A polynomial algorithm for the k-cut problem for fixed k. Math. Oper. Res. 19(1):24–37.Link, Google Scholar
- (2002) Gene selection for cancer classification using support vector machines. Machine Learn. 46(1–3):389–422.Crossref, Google Scholar
- (2004) The entire regularization path for the support vector machine. J. Machine Learn. Res. 5(1):1391–1415.Google Scholar
- (2010a) HPF: Hochbaum's Pseudo-Flow Algorithm Implementation. Software available at http://riot.ieor.berkeley.edu/riot/Applications/Pseudoflow/maxflow.html.Google Scholar
- (2010b) Polynomial time algorithms for ratio regions and a variant of normalized cut. IEEE Trans. Pattern Anal. Machine Intelligence 32(5):889–898.Crossref, Google Scholar
- (2007) Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems Appl. 33(2): 434–440.Crossref, Google 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
- (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.Crossref, Google Scholar
- (1962) General description and operating characteristics of the Berkeley 88-inch cyclotron. Nuclear Instruments Methods 18–19(1):33–40.Crossref, Google Scholar
- (2007) Supervised learning: A review of classification techniques. Informatica 31(1):249–268.Google Scholar
- (2006) Chemometrics and its applications to X-ray spectrometry. X-ray Spectrometry 35(4):215–225.Crossref, Google Scholar
- (2007) Nonlinear knowledge in kernel approximation. IEEE Trans. Neural Networks 18(1):300–306.Crossref, Google Scholar
- (2008) Nonlinear knowledge-based classification. IEEE Trans. Neural Networks 19(10):1826–1832.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2001) PCA versus LDA. IEEE Trans. Pattern Anal. Machine Intelligence 23(2):228–233.Crossref, Google Scholar
- (2004) Discriminant Analysis and Statistical Pattern Recognition (Wiley Interscience, New York).Google Scholar
- (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
- (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.Crossref, Google Scholar
- (2009) Computational methods for analysis of MALDI-TOF spectra to discover peptide serum biomarkers. The Protein Protocols Handbook IV:1175–1183.Crossref, Google Scholar
- (2004) In defense of one-vs-all classication. J. Machine Learn. Res. 5(1):101–141.Google Scholar
- (2006) Hierarchy and adaptivity in segmenting visual scenes. Nature 442(7104): 810–813.Crossref, Google Scholar
- (2000) Normalized cut and image segmentation. IEEE Trans. Pattern Anal. Machine Intelligence 22(8):888–905.Crossref, Google Scholar
- (2009) Using low resolution gamma detectors to detect and differentiate 239Pu and 235U fissions. J. Radioanalytical Nuclear Chemistry 282(3):901–904.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (2000) Neural network credit scoring models. Comput. Oper. Res. 27(11–12):1131–1152.Crossref, Google Scholar
- (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.Crossref, Google Scholar
- (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

