Data processing: artificial intelligence – Machine learning
Reexamination Certificate
2011-06-07
2011-06-07
Starks, Jr., Wilbert L (Department: 2129)
Data processing: artificial intelligence
Machine learning
C706S045000
Reexamination Certificate
active
07958063
ABSTRACT:
A method of identifying and localizing objects belonging to one of three or more classes, includes deriving vectors, each being mapped to one of the objects, where each of the vectors is an element of an N-dimensional space. The method includes training an ensemble of binary classifiers with a CISS technique, using an ECOC technique. For each object corresponding to a class, the method includes calculating a probability that the associated vector belongs to a particular class, using an ECOC probability estimation technique. In another embodiment, increased detection accuracy is achieved by using images obtained with different contrast methods. A nonlinear dimensional reduction technique, Kernel PCA, was employed to extract features from the multi-contrast composite image. The Kernel PCA preprocessing shows improvements over traditional linear PCA preprocessing possibly due to its ability to capture high-order, nonlinear correlations in the high dimensional image space.
REFERENCES:
patent: 4349435 (1982-09-01), Ochiai
patent: 4421716 (1983-12-01), Hench et al.
patent: 6253607 (2001-07-01), Dau
patent: 2002/0165837 (2002-11-01), Zhang et al.
patent: WO-99/08091 (1999-02-01), None
Long, et al, Effective Automatic Recognition of Cultured Cells in Bright Field Images Using Fisher's Linear Discriminant Preprocessing, Image and Vision Computing 23 (2005), pp. 1203-1213.
Long, et al, Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedure, Computers in Biology and Medicine 36 (2006), pp. 339-362.
Mika, et al, Kernel PCA and De-noising in Feature Spaces, Advances in Neural Information Processing Systems 11, 1999, pp. 1-7.
Long et al, A new preprocessing approach for cell recognition, IEEE Trans Inf Technol Biomed., Sep. 2005, pp. 407-412.
A. Ashkin, “Optical trapping and manipulation of neutral particles using lasers”, Proc. Natl. Acad. Sci., 94, 1997, pp. 4853-4860.
A. Berger, Error-Correcting Output Coding for Text Classification, IJCAI'99: Workshop on machine learning for information filtering, Stockholm, Sweden, 1999.
A. Hultgren and M. Tanase, “Cell manipulation using magnetic nanowires”, Journal of Applied Physics, 93 (10), 2003, pp. 7554-7556.
A. Shashua, “On the relationship between the support vector machine for classification and sparsified Fisher's Linear Discriminant”, Neural Processing Letters, 9, 1999, pp. 129-139.
B. Boser, I. Guvlon and V. Vapnik, “A training Algorithm for Optimal Margin Classifiers”, Proceedings of the Fifth Conference on Computational learning Theory, New York: Association of Computing Machinery, 1992, pp. 144-152.
B. Schölkopf, “Statistical learning and kernel methods”, MSR-TR 2000-23, Microsoft Research, 2000.
B. Schölkopf, et al., “Nonlinear component analysis as a kernel eigenvalue problem”, Neural Computation, 10, 1998, pp. 1299-1319.
B. Stuhrmann, et al., “Automated tracking and laser micromanipulation of motile cells”, Review of Scientific Instruments, 76, 2005, 035105.
C. Burges, A tutorial on Support Vector Machines for pattern recognition, Data Mining and Knowledge Discovery, vol. 2, 1998, 122-167.
C. Campbell and N. Cristianini, “Simple training algorithms for support vector machines”, Technical Report CIG-TR-KA, University of Bristol, Engineering Mathematics, Computational Intelligence Group, 1999.
C. Loukas, et al., “An Image Analysis-based Approach for Automated Counting of Cancer Cell Nuclei in Tissue Sections”, Cytometry Part A, 55, 2003, pp. 30-42.
C. Moler and G. Stewart, “An algorithm for generalized matrix eigenvalue problems”, SIAM J. Numer. Anal., 10, 1973, pp. 99-130.
Chih-Chung Chang and Chih-Jen Lin, LIBSVM—A Library for Support Vector Machines, <http://www.csie.ntu.edu.tw/˜cjlin/libsvm/>, Copyright 2000-2010.
International Search Report and Written Opinion mailed on Sep. 20, 2006 for corresponding International Patent Application No. PCT/US2005/040905.
Invitation to Pay Additional Fees mailed by International Searching Authority/European Patent Office on Jun. 21, 2006 for corresponding International Patent Application No. PCT/US2005/040905.
D. Aha and R. Bankert, Cloud classification using error-correcting output codes, Artificial Intelligence Applications: Natural Resources, Agriculture, and Environmental Science, vol. 11, No. 1, 1997, 13-28.
D. Gray, et al., “Dielectrophoretic registration of living cells to a microelectrode array”, Biosensors and Bioelectronics, 19, 2004, pp. 1765-1774.
D. Haliyo, S. Regnier and J.-C. Guinot, “MAD, the adhesion based dynamic micro-manipulator”, European Journal of Mechanics A/Solids 22, 2003, pp. 903-916.
D.J.M. Tax and R.P.W. Duin, Using Two-Class Classifiers for Multiclass Classification, ICPR16: Proc. 16th Int. Conf. on Pattern Recognition, Quebec City, Canada, 2002, 124-127.
D.P. Chakraborty, Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data, Medical Physics, vol. 16, 1989, 561-568.
E. Allwein, R. Schapire, and Y. Singer, Reducing multiclass to binary: A unifying approach for margin classifiers, Journal of Machine Learning Research, vol. 1, 2000, 113-141.
E. Kong and T. Dietterich, Error-correcting output coding corrects bias and variance, Proceedings of the 12th International Conference on Machine Learning, 1995, 313-321.
E. Osuna, R. Freund, and F. Girosi. “Support Vector Machines: Training and Applications”, A.I. Memo 1602, MIT A.I. Lab., 1997.
F. Arai, et al, “Minimally Invasive Micromanipulation of Microbe by Laser Trapped Micro Tools”, Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, 2002, pp. 1937-1942.
F. Rosenblatt, “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain”, Cornell Aeronautical Laboratory, Psychological Review, 65(6), 1958, pp. 386-408.
F. Schnorrenberg, C. Pattichis, K. Kyriacou, and C. Schizas, “Computer-aided detection of breast cancer nuclei”, IEEE Trans. on Inf. Techn. in Biomedicine 1(2), 1997, pp. 128-140.
G. Bakiri and T. Dietterich, Achieving high-accuracy text-to-speech with machine learning, Data mining in speech synthesis, Kluwer Academic Publishers, Boston, MA, 1999.
G. James and T. Hastie, The error coding method and PiCTs, Journal of Computational and Graphical Statistics, vol. 7, No. 3, 1997, 377-387.
G. Valentini and F. Masulli, Ensembles of Learning Machines, Neural Nets WIRN Vietri-02, Series Lecture Notes in Computer Sciences, Springer-Verlag, Heidelberg, Germany, 2002.
H. Lee, T. Hunt, and R. Westervelt, “Magnetic and Electric Manipulation of a Single Cell in Fluid”, Materials Research Society Symposium Proceeding, 820, 2004.
H. Murase and S. Nayar, “Visual learning and recognition of 3D objects from appearance”, Intl. J. Computer Vision, 14, 1995, pp. 5-24.
J. Drish, “Obtaining Calibrated Probability Estimates from Support Vector Machines”, technique report, Dept. of Computer Science and Engineering, University of California, San Diego, CA, 2001.
J. Kittler, et al., Face Verification Using Error Correcting Output Codes, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR01), 2001, 755-760.
J. Meltzer, et al., “Multiple view feature descriptors from image sequences via kernel principal component analysis”, Computer Vision—ECCV 2004, Pt 1. 3021, 2004, pp. 215-227.
J. Mendes, et al., “Algorithms for pattern recognition in images of cell cultures”, Proceedings of SPIE, 4425, 2001, pp. 282-290.
J. Platt, “Fast Training of Support Vector Machines using Sequential Minimal Optimization”, in B. Schölkopf, C. Burges, and A. Smola (eds): Advances in Kernel Methods—Support Vector Learning, MIT Press, 1998.
J. Platt, “Sequential Minimal Opti
Cleveland W. Louis
Long Xi
Yao Y. Lawrence
Starks, Jr. Wilbert L
Trustees of Columbia University in the City of New York
Wilmer Cutler Pickering Hale and Dorr LLP
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