CONTENTS

References

[1]     S. Winawer, R. Fletcher, D. Rex, J. Bond, R. Burt, J. Ferrucci, T. Ganiats, T. Levin, S. Woolf, D. Johnson, L. Kirk, S. Litin, and C. Simmang, "Colorectal cancer screening and surveillance: clinical guidelines and rationale-Update based on new evidence," Gastroenterology, vol. 124, pp. 544-60, Feb 2003.

[2]     K. D. Bodily, J. G. Fletcher, T. Engelby, M. Percival, J. A. Christensen, B. Young, A. J. Krych, D. C. Vander Kooi, D. Rodysill, J. L. Fidler, and C. D. Johnson, "Nonradiologists as second readers for intraluminal findings at CT colonography," Acad Radiol, vol. 12, pp. 67-73, Jan 2005.

[3]     J. G. Fletcher, F. Booya, C. D. Johnson, and D. Ahlquist, "CT colonography: unraveling the twists and turns," Curr Opin Gastroenterol, vol. 21, pp. 90-8, Jan 2005.

[4]     H. Yoshida and A. H. Dachman, "CAD techniques, challenges, and controversies in computed tomographic colonography," Abdom Imaging, vol. 30, pp. 26-41, Jan-Feb 2005.

[5]     H. Yoshida and J. Näppi, "Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps," IEEE Trans Med Imaging, vol. 20, pp. 1261-74, Dec 2001.

[6]     R. M. Summers, C. F. Beaulieu, L. M. Pusanik, J. D. Malley, R. B. Jeffrey, Jr., D. I. Glazer, and S. Napel, "Automated polyp detector for CT colonography: feasibility study," Radiology, vol. 216, pp. 284-90, 2000.

[7]     R. M. Summers, M. Franaszek, M. T. Miller, P. J. Pickhardt, J. R. Choi, and W. R. Schindler, "Computer-aided detection of polyps on oral contrast-enhanced CT colonography," AJR Am J Roentgenol, vol. 184, pp. 105-8, Jan 2005.

[8]     G. Kiss, J. Van Cleynenbreugel, M. Thomeer, P. Suetens, and G. Marchal, "Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods," Eur Radiol, vol. 12, pp. 77-81, Jan 2002.

[9]     D. S. Paik, C. F. Beaulieu, G. D. Rubin, B. Acar, R. B. Jeffrey, Jr., J. Yee, J. Dey, and S. Napel, "Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT," IEEE Trans Med Imaging, vol. 23, pp. 661-75, Jun 2004.

[10]   A. K. Jerebko, R. M. Summers, J. D. Malley, M. Franaszek, and C. D. Johnson, "Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees," Med Phys, vol. 30, pp. 52-60, Jan 2003.

[11]   J. Näppi, H. Frimmel, A. H. Dachman, and H. Yoshida, "A new high-performance CAD scheme for the detection of polyps in CT colonography," Medical Imaging 2004: Image Processing, 2004, pp. 839-848.

[12]   A. K. Jerebko, J. D. Malley, M. Franaszek, and R. M. Summers, "Multiple neural network classification scheme for detection of colonic polyps in CT colonography data sets," Acad Radiol, vol. 10, pp. 154-60, Feb 2003.

[13]   A. K. Jerebko, J. D. Malley, M. Franaszek, and R. M. Summers, "Support vector machines committee classification method for computer-aided polyp detection in CT colonography," Acad Radiol, vol. 12, pp. 479-86, Apr 2005.

[14]   V. N. Vapnik, The nature of statistical learning theory, 2nd ed. New York: Springer, 2000.

[15]   R. Courant and D. Hilbert, "Methods of Mathematical Physics," vol. 1, pp. 138-140, 1966.

[16]   O. L. Mangasarian, A.J. Smola, and B. Schölkopf, "Sparse kernel feature analysis," University of Wisconsin, Tech. Rep. 99-04, 1999.

[17]   J. Franc and V. Hlavac, "Statistical Pattern Recognition Toolbox for Matlab," http://cmp.felk.cvut.cz/~xfrancv/stprtool/, 2004.

[18]   [On-line], "Partners Research Computing," http://www.partners.org/rescomputing/, 2006.

[19]   A. J. Smola, B. Schölkopf, "Sparse Greedy Matrix Approximation for Machine Learning", Proc. 17th International Conf. on Machine Learning, 2000.

[20]   X. Jiang, Y. Motai, R. Snapp, and X. Zhu, Accelerated Kernel Feature Analysis, Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 109-116, 2006.

[21] B. Schölkopf and A. J. Smola, Learning with Kernels, MIT Press, 2002.

[22]  K. Fukunaga and L. Hostetler. Optimization of k-nearest neighbor density estimates. IEEE transactions

        on Information Theory, 19(3):320–326, 1973    

[23]  J.H. Friedman. Flexible metric nearest neighbor classification. Technical report, Department of

         Statistics, Stanford University, Stanford, CA, USA, November 1994.

[24]  T. Hastie and R. Tibshirani. Discriminant adaptive nearest neighbor classification. IEEE Transactions  

        on Pattern Analysis and Machine Intelligence, 18(6):607–616, 1996.

[25]  D.G. Lowe. Similarity metric learning for a variable-kernel classifier. Neural Computation, 7(1):72–85,

       1995.

[26]  J. Peng, D.R. Heisterkamp, and H.K. Dai. Adaptive kernel metric nearest neighbor classification. In  

        Proceedings of the Sixteenth International Conference on Pattern Recognition, volume 3, pages

        33–36, Qu´ebec City, Qu´ebec, Canada, 11–15 August 2002.

[27] Q.B. Gao, Z.Z. Wang, Center-based nearest neighbor classifier, Pattern Recognition 40(2007) 346–349.

[28] S. Li, J. Lu, Face recognition using the nearest feature line method, IEEE Trans. Neural Networks 10 (2)

      (1999) 439–443.

[29] P. Vincent, Y. Bengio, K-local hyperplane and convex distance nearest neighbor algorithms, Advances in

      Neural Information Processing Systems (NIPS), vol.14, MIT Press, Cambridge, MA,2002, pp. 985–992.

[30] W. Zheng, L. Zhao, C. Zou, Locally nearest neighbor classifiers for pattern classification, Pattern

       Recognition 37 (2004) 1307–1309.

[31] Theodoros Damoulas* and Mark A. Girolami, Probabilistic multi-class multi-kernel learning: on protein

       fold recognition and remote homology detection, Vol. 24 no. 10 2008, pages 1264–1270,

       doi:10.1093/bioinformatics/btn112. 

[32] S. Amari and S. Wu,”Improving Support Vector Machine Classifiers by Modifying Kernel Functions”,

      Neural Networks, Vol.6, pp.783-789, 1999.

[33] B. Souza and A. de Carvalho,” Gene selection based on multi-class support vector machines and genetic

       algorithms”, Molecular Research, Vol 4, NO.3, pp. 599-607, 2005.

[34] B. Schölkopf and A. J. Smola, Learning with kernels, MIT Press, pp. 211-214, 2002.

[35] Huilin Xiong, Ya Zhang, and Xue-Wen Chen Data-Dependent Kernel Machines for Microarray Data

       Classification. In Proceedings of IEEE/ACM Transactions on Computational Biology and

       Binformatics, vol. 4, NO. 4,   October-December  2007.

[36] H. Xiong, M.N.S. Swamy, and M.O. Ahmad, “Optimizing the Data-Dependent Kernel in the Empirical

       Feature Space,” IEEE Trans. Neural Networks, vol. 16, pp. 460-474, 2005.

[37] G.C. Cawley, MATLAB Support Vector Machine Toolbox, School of Information Systems, Univ. of

      East Anglia, http://theoval.sys.uea.ac.uk/~gcc/svm/ toolbox, Norwich, U.K., 2000

[38] Y. Raviv and N. Intrator, “Bootstrapping with Noise: An Efficient Regularization Technique,” 

         Connection Science, vol. 8, pp. 355-372, 1996.

[39] Hans Anton Buchholdt “Structural Dynamics For Engineers” Published by Thomas Telford, 1997

       ISBN 0727725599.

[40] Richard O. Duda, Peter E. Hart, David G. Stork.: Pattern Classification (2nd Edition), John Wiley &

       Sons Inc., 2001.

[41] Bernhard Schökopf , Alexander J. Smola.: Learning with Kernels: Support Vector Machines,

       Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning), MIT press,

       2002

[42] H. Fröhlich, O. Chapelle, B. Scholkopf.: Feature selection for support vector machines by means of

       genetic algorithm, Tools with Artificial Intelligence, Proceedings. 15th. IEEE International Conference,

       pp. 142 – 148, 2003

[43] Xue-wen Chen.: Gene selection for cancer classification using bootstrapped genetic algorithms and

       support vector machines, The Computational Systems, Bioinformatics Conference. Proceedings IEEE

       International Conference, pp. 504 – 505, 2003.

[44] Chanho Park and Sung-Bae Cho.: Genetic search for optimal ensemble of feature-classifier pairs in

       DNA gene expression profiles, Neural Networks, 2003. Proceedings of the International Joint   

       Conference, vol.3, pp. 1702 – 1707, 2003.

[45] Firooz A. Sadjadi “Polarimetric Radar Target Classification Using Support Vector Machines” Optical

       engineering  47(4), 046201 April 2008.

[46] Tom Briggs, Tim Oates, “Discovering Domain Specific Composite Kernels”.

 

 

 

 

 

 

 

 

 

 

 

 

 



S. Myla is with the School of Engineeing, Virginia Commonwealth University, Richmond, VA 23284 USA.

[†] Y. Motai is with the School of Engineering, Virginia Commonwealth University, Richmond, VA 23284 USA.

A. Docef is with the School of Engineering, Virginia Commonwealth University, Richmond, VA 23284 USA.

[‡] J. Näppi is with the Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA.

[§] H. Yoshida is with the Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA.