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Volumn 48, Issue 2, 2004, Pages 223-236

Parameter detection of thin films from their X-ray reflectivity by support vector machines

Author keywords

Optical matrix method; Radial basis functions; Reproducing kernel Hilbert spaces; Support vector machines; X ray reflectometry

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; AUTOMATION; BACKPROPAGATION; COMPUTER SIMULATION; CURVE FITTING; INTEGRATED CIRCUITS; MATRIX ALGEBRA; QUADRATIC PROGRAMMING; REGRESSION ANALYSIS; SURFACE ROUGHNESS; THIN FILMS; X RAYS;

EID: 0348046424     PISSN: 01689274     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apnum.2003.07.002     Document Type: Article
Times cited : (8)

References (28)
  • 1
    • 0346131481 scopus 로고
    • The new Siemens X-ray reflectometer - A tool with outstanding capabilities
    • Siemens
    • Siemens, The new Siemens X-ray reflectometer - A tool with outstanding capabilities, Siemens Report, 1994.
    • (1994) Siemens Report
  • 4
    • 26144449160 scopus 로고
    • Surface studies of solids by total reflection of X-rays
    • Parratt L.G. Surface studies of solids by total reflection of X-rays. Phys. Rev. 95:1954;359-370.
    • (1954) Phys. Rev. , vol.95 , pp. 359-370
    • Parratt, L.G.1
  • 5
    • 0000515073 scopus 로고
    • Caractérisation des surfaces par réflexion rasante de rayons X. Application à l'étude du polissage de quelques verres silicates
    • Névot L., Croce P. Caractérisation des surfaces par réflexion rasante de rayons X. Application à l'étude du polissage de quelques verres silicates. Rev. Phys. App. 15:1980;761-779.
    • (1980) Rev. Phys. App. , vol.15 , pp. 761-779
    • Névot, L.1    Croce, P.2
  • 6
    • 0021444811 scopus 로고
    • Metallic multilayers for X-rays using classical thin-film theory
    • Vidal B., Vincent P. Metallic multilayers for X-rays using classical thin-film theory. Appl. Optics. 23:1984;1794-1801.
    • (1984) Appl. Optics , vol.23 , pp. 1794-1801
    • Vidal, B.1    Vincent, P.2
  • 9
    • 0001873883 scopus 로고    scopus 로고
    • Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV
    • B. Schölkopf, C.J.C. Burges, & A.J. Smola. Cambridge, MA: MIT Press
    • Wahba G. Support vector machines, reproducing kernel Hilbert spaces and the randomized GACV. Schölkopf B., Burges C.J.C., Smola A.J. Advances in Kernel Methods - Support Vector Learning. 1999;293-306 MIT Press, Cambridge, MA.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 293-306
    • Wahba, G.1
  • 10
    • 0003554988 scopus 로고    scopus 로고
    • Support vector machines for large-scale regression problems
    • Institut Dalle Molle d'Intelligence Artificelle Perceptive, Martigny, Switzerland
    • R. Collobert, S. Bengio, Support vector machines for large-scale regression problems, Technical Report IDIAP-RR-00-17, Institut Dalle Molle d'Intelligence Artificelle Perceptive, Martigny, Switzerland, 2000.
    • (2000) Technical Report , vol.IDIAP-RR-00-17
    • Collobert, R.1    Bengio, S.2
  • 11
    • 0000913324 scopus 로고    scopus 로고
    • SVMTorch: Support vector machines for large-scale regression problems
    • Collobert R., Bengio S. SVMTorch: Support vector machines for large-scale regression problems. J. Machine Learning Res. 1:2001;143-160.
    • (2001) J. Machine Learning Res. , vol.1 , pp. 143-160
    • Collobert, R.1    Bengio, S.2
  • 12
    • 84956251753 scopus 로고
    • Interferenz von Röntgenstrahlen an dünnen Schichten
    • Kiessig H. Interferenz von Röntgenstrahlen an dünnen Schichten. Ann. Phys. 10:1931;769-778.
    • (1931) Ann. Phys. , vol.10 , pp. 769-778
    • Kiessig, H.1
  • 14
    • 0000085844 scopus 로고
    • X-ray reflection, a new tool for investigating layered structures and interfaces
    • C.S. Barrett, R. Jenkins, J.V. Gilfrich, HuangT.C. New York: Plenum Press
    • Lengeler B. X-ray reflection, a new tool for investigating layered structures and interfaces. Barrett C.S., Jenkins R., Gilfrich J.V., Huang T.C. Advances in X-Ray Analysis. vol. 35:1992;127-135 Plenum Press, New York.
    • (1992) Advances in X-ray Analysis , vol.35 , pp. 127-135
    • Lengeler, B.1
  • 15
    • 33744549305 scopus 로고    scopus 로고
    • An artificial neural network analysis of low-resolution X-ray fluorescence spectra
    • CD-ROM
    • Long X., Huang N., He F., Peng X. An artificial neural network analysis of low-resolution X-ray fluorescence spectra. Adv. X-Ray Anal. 40:1997;. CD-ROM.
    • (1997) Adv. X-ray Anal. , vol.40
    • Long, X.1    Huang, N.2    He, F.3    Peng, X.4
  • 16
    • 0033518786 scopus 로고    scopus 로고
    • Evaluation of residual stress gradients by diffraction methods with wavelets, a neural network approach
    • G.Y. Baaklini, C.A. Lebowitz, & E.S. Boltz. Nondestructive Evaluation Techniques for Aging Infrastructure & Manufacturing Bellington, WA: SPIE
    • Wern H., Ringeisen M. Evaluation of residual stress gradients by diffraction methods with wavelets, a neural network approach. Baaklini G.Y., Lebowitz C.A., Boltz E.S. Nondestructive Evaluation Techniques for Aging Infrastructure & Manufacturing. Proc. of SPIE. vol. 3585:1999;318-328 SPIE, Bellington, WA.
    • (1999) Proc. of SPIE , vol.3585 , pp. 318-328
    • Wern, H.1    Ringeisen, M.2
  • 17
    • 5844297152 scopus 로고
    • Theory of reproducing kernels
    • Aronszajn N. Theory of reproducing kernels. Trans. Amer. Math. Soc. 68:1950;337-404.
    • (1950) Trans. Amer. Math. Soc. , vol.68 , pp. 337-404
    • Aronszajn, N.1
  • 18
    • 0015000439 scopus 로고
    • Some results on Tchebycheffian spline functions
    • Kimeldorf G.S., Wahba G. Some results on Tchebycheffian spline functions. J. Anal. Appl. 33:1971;82-95.
    • (1971) J. Anal. Appl. , vol.33 , pp. 82-95
    • Kimeldorf, G.S.1    Wahba, G.2
  • 20
    • 0003120218 scopus 로고    scopus 로고
    • Fast training of support vector machines using sequential minimal optimization
    • B. Schölkopf, C. Burges, & A.J. Smola. Cambridge, MA: MIT Press
    • Platt J.C. Fast training of support vector machines using sequential minimal optimization. Schölkopf B., Burges C., Smola A.J. Advances in Kernel Methods - Support Vector Learning. 1999;185-208 MIT Press, Cambridge, MA.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 185-208
    • Platt, J.C.1
  • 21
    • 0038400265 scopus 로고    scopus 로고
    • Efficient SVM regression training with SMO
    • NEC Research Institute
    • G.W. Flake, S. Lawrence, Efficient SVM regression training with SMO, Technical Report, NEC Research Institute, 1999.
    • (1999) Technical Report
    • Flake, G.W.1    Lawrence, S.2
  • 23
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • B. Schölkopf, C. Burges, & A.J. Smola. Cambridge, MA: MIT Press
    • Joachims T. Making large-scale support vector machine learning practical. Schölkopf B., Burges C., Smola A.J. Advances in Kernel Methods - Support Vector Learning. 1999;169-184 MIT Press, Cambridge, MA.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 24
    • 0009051052 scopus 로고    scopus 로고
    • On the convergence of SVMTorch, an algorithm for large-scale regression problems
    • Institut Dalle Molle d'Intelligence Artificelle Perceptive, Martigny, Switzerland
    • R. Collobert, S. Bengio, On the convergence of SVMTorch, an algorithm for large-scale regression problems, Technical Report IDIAP-RR-00-24, Institut Dalle Molle d'Intelligence Artificelle Perceptive, Martigny, Switzerland, 2000.
    • (2000) Technical Report , vol.IDIAP-RR-00-24
    • Collobert, R.1    Bengio, S.2
  • 25
    • 0035506741 scopus 로고    scopus 로고
    • On the convergence of the decomposition method for support vector machines
    • Lin C.-J. On the convergence of the decomposition method for support vector machines. IEEE Trans. Neural Networks. 12:2001;1288-1298.
    • (2001) IEEE Trans. Neural Networks , vol.12 , pp. 1288-1298
    • Lin, C.-J.1
  • 26
    • 0008197560 scopus 로고    scopus 로고
    • On the noise model of support vector machine regression
    • MIT, Artificial Intelligence Laboratory, Cambridge, MA
    • M. Pontil, S. Mukherjee, F. Girosi, On the noise model of support vector machine regression. A.I. Memo No. 1651, MIT, Artificial Intelligence Laboratory, Cambridge, MA, 1998.
    • (1998) A.I. Memo No. 1651 , vol.1651
    • Pontil, M.1    Mukherjee, S.2    Girosi, F.3
  • 27
    • 84879910175 scopus 로고    scopus 로고
    • From regularization operators to support vector kernels
    • M.I. Jordan, M.J. Kearns, SollaS.A. San Mateo, CA: Brad Gord
    • Smola A.J., Schölkopf B. From regularization operators to support vector kernels. Jordan M.I., Kearns M.J., Solla S.A. Advances in Neural Information Processing Systems. vol. 10:1998;343-349 Brad Gord, San Mateo, CA.
    • (1998) Advances in Neural Information Processing Systems , vol.10 , pp. 343-349
    • Smola, A.J.1    Schölkopf, B.2
  • 28
    • 0008267187 scopus 로고    scopus 로고
    • Dynamically adapting kernels in support vector machines
    • Royal Holloway College, University of London, London
    • N. Cristianini, C. Campbell, J. Shawe-Taylor, Dynamically adapting kernels in support vector machines, NeuroCOLT Technical Report NC-TR-98-017, Royal Holloway College, University of London, London, 1998.
    • (1998) NeuroCOLT Technical Report , vol.NC-TR-98-017
    • Cristianini, N.1    Campbell, C.2    Shawe-Taylor, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.