참고문헌 (28)

1. Signal Process. Markou 83 12 2481 2003 10.1016/j.sigpro.2003.07.018 Novelty detection: a review, part 1: statistical approaches 

   상세보기

2. Signal Process. Markou 83 12 2499 2003 10.1016/j.sigpro.2003.07.019 Novelty detection: a review, part 2: neural network based approaches 

   상세보기

3. 10.1049/cp:19950597 L. Tarassenko, P. Hayton, N. Cerneaz, M. Brady, Novelty detection for the identification of masses in mammograms, in: Proceedings of the Fourth IEE International Conference on Artificial Neural Networks, IEE, London, 1995, pp. 442-447. 

   

4. Pattern Recognition Cheng 39 4 646 2006 10.1016/j.patcog.2005.07.006 Approaches for automated detection and classification of masses in mammograms 

   상세보기

5. Scholkopf 2002 Learning with Kernels 

6. Adv. Neural Inf. Process. Syst. Scholkopf 12 582 2000 Support vector method for novelty detection 

7. 10.1162/089976601750264965 B. Scholkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, R.C. Williamson, Estimating the support of a high-dimensional distribution, Neural Comput. 13. 

   상세보기

8. Pattern Recognition Lett. Tax 20 1191 1999 10.1016/S0167-8655(99)00087-2 Support vector domain description 

   상세보기

9. D.M.J. Tax, P. Juszczak, Kernel whitening for one-class classification, Lecture Notes in Computer Science, vol. 2388, 2002, pp. 40-52. 

10. L. Tarassenko, A. Nairac, N. Townsend, P. Cowley, Novelty detection in jet engines, in: IEE Colloquium on Condition Monitoring: Machinery, External Structures and Health (Ref. No. 1999/034), IEE, London, 1999, pp. 4/1-4/5. 

11. Song 577 2001 Proceedings of the 14th International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management (COMADEM) Analysis of novelty detection properties of autoassociators 

12. Neural Comput. Scholkopf 10 1299 1998 10.1162/089976698300017467 Nonlinear component analysis as a kernel eigenvalue problem 

    상세보기

13. Wald 1950 Statistical Decision Functions 

14. Lusted 1968 Introduction to Medical Decision Making 

15. Ann. Math. Stat. Parzen 33 1065 1962 10.1214/aoms/1177704472 On estimation of a probability density function and mode 

    상세보기

16. Diamantaras 1996 Principal Component Neural Networks 

17. Y. LeCun, The MNIST database of handwritten digits, 〈http://yann.lecun.com/exdb/mnist/〉, 1998. 

18. C. Blake, C. Merz, UCI repository of machine learning databases, 〈http://www.ics.uci.edu/∼mlearn/MLRepository.html〉, 1998. 

19. Proc. Natl. Acad. Sci. USA Wolberg 87 9193 1990 10.1073/pnas.87.23.9193 Multisurface method of pattern separation for medical diagnosis applied to breast cytology 

    상세보기

20. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines, 〈http://www.csie.ntu.edu.tw/∼cjlin/libsvm〉, 2001. 

21. T. Takahashi, T. Kurita, Robust de-noising by kernel PCA, in: Proceedings of the International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, vol. 2415, Springer, Berlin, 2002, pp. 739-744. 

22. C.-D. Lu, T.-Y. Zhang, X.-Z. Du, C.-P. Li, A robust kernel PCA algorithm, in: Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 5, IEEE, 2004, pp. 3084-3087. 

23. Press 1993 Numerical Recipes in C: The Art of Scientific Computing 

24. Burges 71 1996 Proceedings of the 13th International Conference on Machine Learning Simplified support vector decision rules 

25. Scholkopf 124 1998 20. DAGM Symposium Mustererkennung Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature spaces 

26. Adv. Neural Inf. Process. Syst. Mika 11 536 1999 Kernel PCA and de-noising in feature spaces 

27. Neural Comput. Tipping 11 443 1999 10.1162/089976699300016728 Mixtures of probabilistic principal component analyzers 

    상세보기

28. Science Roweis 290 2323 2000 10.1126/science.290.5500.2323 Nonlinear dimensionality reduction by locally linear embedding 

    상세보기