Kristin P. Bennett
- {1} Barabino N., Pallavicini M., Petrolini A., Pontil M. and Verri A. Support vector machines vs multi-layer perceptrons in particle identification. In Proceedings of the European Symposium on Artifical Neural Networks '99 (D-Facto Press, Belgium), p. 257-262, 1999.Google Scholar
- {2} Bennett K. and Bredensteiner E. Geometry in Learning, in Geometry at Work, C. Gorini Editor, Mathematical Association of America, Washington D. C., 132-145, 2000.Google Scholar
- {3} Bennett K. and Bredensteiner E. Duality and Geometry in SVMs. In P. Langley editor, Proc. of 17th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, 65-72, 2000. Google ScholarDigital Library
- {4} Bennett K., Dermiriz A. and Shawe-Taylor J. A Column Generation Algorithm for Boosting. In P. Langley editor, Proc. of 17th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, 57-64, 2000. Google ScholarDigital Library
- {5} Bennett K., Wu D. and Auslender L. On support vector decision trees for database marketing. Research Report No. 98-100, Rensselaer Polytechnic Institute, Troy, NY, 1998.Google Scholar
- {6} Bradley P., Mangasarian O. and Musicant, D. Optimization in Massive Datasets. To appear in Abello, J., Pardalos P., Resende, M (eds), Handbook of Massive Datasets, Kluwer, 2000. Google ScholarDigital Library
- {7} Brown M., Grundy W., D. Lin, N. Cristianini, C. Sugnet, T. Furey, M. Ares Jr. D. Haussler. Knowledge-based Analysis of Microarray Gene Expression Data using Support Vector Machines. Proceedings of the National Academy of Sciences, 97 (1), p. 262-267, 2000.Google ScholarCross Ref
- {8} Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, p. 121-167, 1998. Google ScholarDigital Library
- {9} Campbell C. and Bennett K. A Linear Programming Approach to Novelty Detection. To appear in Advances in Neural Information Processing Systems 14 (Morgan Kaufmann, 2001).Google Scholar
- {10} Chapelle O. and Vapnik V. Model selection for support vector machines. To appear in Advances in Neural Information Processing Systems, 12, ed. S. A. Solla, T. K. Leen and K.-R. Muller, MIT Press, 2000. Google ScholarDigital Library
- {11} Cortes C. and Vapnik V. Support vector networks. Machine Learning 20, p. 273-297, 1995. Google ScholarDigital Library
- {12} Crisp D. and Burges C. A geometric interpretation of v-svm classifiers. Advances in Neural Information Processing Systems, 12, ed. S. A. Solla, T. K. Leen and K.-R. Muller, MIT Press, 2000.Google Scholar
- {13} Cristianini N., Campbell C. and Shawe-Taylor, J. Dynamically adapting kernels in support vector machines. Advances in Neural Information Processing Systems, 11, ed. M. Kearns, S. A. Solla, and D. Cohn, MIT Press, p. 204-210, 1999. Google ScholarDigital Library
- {14} Cristianini N. and Shawe-Taylor J. An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, 2000. www.support-vector.net. Google ScholarDigital Library
- {15} Collobert R. and Bengio S. SVMTorch web page, http://www.idiap.ch/learning/SVMTorch.htmlGoogle Scholar
- {16} DeCoste D. and Scholkopf B. Training Invariant Support Vector Machines. To appear in Machine Learning (Kluwer, 2001). Google ScholarDigital Library
- {17} Drucker H., with Wu D. and Vapnik V. Support vector machines for spam categorization. IEEE Trans. on Neural Networks, 10, p. 1048-1054. 1999. Google ScholarDigital Library
- {18} Drucker H., Burges C., Kaufman L., Smola A. and Vapnik V. Support vector regression machines. In: M. Mozer, M. Jordan, and T. Petsche (eds.). Advances in Neural Information Processing Systems, 9, MIT Press, Cambridge, MA, 1997.Google Scholar
- {19} Dumais S., Platt J., Heckerman D. and Sahami M. Inductive Learning Algorithms and Representations for Text Categorization. 7th International Conference on Information and Knowledge Management, 1998. Google ScholarDigital Library
- {20} Fernandez R. and Viennet E. Face identification using support vector machines. Proceedings of the European Symposium on Artificial Neural Networks (ESANN99), (D.- Facto Press, Brussels) p. 195-200, 1999.Google Scholar
- {21} Ferris, M. and Munson T. Semi-smooth support vector machines. Data Mining Institute Technical Report 00-09, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, 2000.Google Scholar
- {22} Ferris M. and Munson T. Interior point methods for massive support vector machines. Data Mining Institute Technical Report 00-05, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, 2000.Google Scholar
- {23} Friess T.-T., Cristianini N. and Campbell, C. The kernel adatron algorithm: a fast and simple learning procedure for support vector machines. 15th Intl. Conf. Machine Learning, Morgan Kaufman Publishers, p. 188-196, 1998. Google ScholarDigital Library
- {24} Furey T., Cristianini N., Duffy N., Bednarski D., Schummer M. and Haussler D. Support Vector Machine Classification and Validation of Cancer Tissue Samples using Microarray Expression Data. Bioinformatics 16 p. 906-914, 2000.Google ScholarCross Ref
- {25} Golub T., Slonim D., Tamayo P., Huard C., Gassenbeek M., Mesirov J., Coller H., Loh M., Downing J., Caligiuri M., Bloomfield C. and Lander E. Modecular Classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286 p. 531-537, 1999.Google ScholarCross Ref
- {26} Guyon I., Matic N. and Vapnik V. Discovering informative patterns and data cleaning. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, MIT Press, p. 181- 203, 1996. Google ScholarDigital Library
- {27} Guyon, I Web page on SVM Applications, http://www.clopinet.com/isabelle/Projects/SVM/applist.htmlGoogle Scholar
- {28} Jaakkola T., Diekhans M. and Haussler, D. A discriminative framework for detecting remote protein homologies. MIT Preprint, 1999.Google Scholar
- {29} Joachims, T. Text categorization with support vector machines: learning with many relevant features. Proc. European Conference on Machine Learning (ECML), 1998. Google ScholarDigital Library
- {30} Joachims, T. Estimating the Generalization Performance of an SVM efficiently. In Proceedings of the 17th International Conference on Machine Learning, Morgan Kaufmann,. 431-438, 2000. Google ScholarDigital Library
- {31} Joachims, T. Text categorization with support vector machines: learning with many relevant features. Proc. European Conference on Machine Learning (ECML), 1998. Google ScholarDigital Library
- {32} Joachims, T. Web Page on SVMLight: http://www.ai.cs.uni-dortmund.de /SOFTWARE/SVM_LIGHT/svm_light.eng.htmlGoogle Scholar
- {33} Keerthi S., Shevade S., Bhattacharyya C. and Murthy, K. Improvements to Platt's SMO algorithm for SVM classifier design. Tech Report, Dept. of CSA, Banglore, India, 1999.Google Scholar
- {34} Keerthi S., Shevade, S., Bhattacharyya C. and Murthy, K. A. Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design, Techical Report TR-ISL-99-03, Intelligent Systems Lab, Dept of Computer Science and Automation, Indian Institute of Science, Bangalore, India, (accepted for publication in IEEE Transaction on Neural Networks) 1999.Google ScholarDigital Library
- {35} Luenberger, D. Linear and Nonlinear Programming. Addison-Wesley, 1984.Google Scholar
- {36} Mangasarian, O. and Musicant D. Massive Support Vector Regression Data mining Institute Technical Report 99-02, Dept of Computer Science, University of Wisconsin-Madison, August 1999.Google Scholar
- {37} Mangasarian, O. and Musicant D. Lagrangian Support Vector Regression Data mining Institute Technical Report 00-06, June 2000.Google Scholar
- {38} Mukherjee S., Tamayo P., Slonim D., Verri A., Golub T., Mesirov J. and Poggio T. Support Vector Machine Classification of Microarray Data, MIT AI Memo No. 1677 and MIT CBCL Paper No. 182.Google Scholar
- {39} ORL dataset: Olivetti Research Laboratory, 1994,. http://www.uk.research.att.com/facedatabase.htmlGoogle Scholar
- {40} Osuna E., Freund R. and Girosi F. Training Support Vector Machines: an Application to Face Detection. Proceedings of CVPR'97, Puerto Rico, 1997. Google ScholarDigital Library
- {41} Osuna E., Freund R. and Girosi F. Proc. of IEEE NNSP, Amelia Island, FL p. 24-26, 1997.Google Scholar
- {42} Osuna E. and Girosi F. Reducing the Run-time Complexity in Support Vector Machines. In B. Scholkopf, C. Burges and A. Smola (ed.), Advances in Kernel Methods: Support Vector Learning, MIT press, Cambridge, MA, p. 271-284, 1999. Google ScholarDigital Library
- {43} Platt J. Fast training of SVMs using sequential minimal optimization. In B. Scholkopf, C. Burges and A. Smola (ed.), Advances in Kernel Methods: Support Vector Learning, MIT press, Cambridge, MA, p. 185-208, 1999. Google ScholarDigital Library
- {44} Papageorgiou C., Oren M. and Poggio, T. A General Framework for Object Detection. Proceedings of International Conference on Computer Vision, p. 555-562, 1998. Google ScholarDigital Library
- {45} Raetsch G., Demiriz A., and Bennett K. Sparse regression ensembles in infinite and finite hypothesis space. NeuroCOLT2 technical report, Royal Holloway College, London, September, 2000.Google Scholar
- {46} Rychetsky M., Ortmann, S. and Glesner, M. Support Vector Approaches for Engine Knock Detection. Proc. International Joint Conference on Neural Networks (IJCNN 99), July, 1999, Washington, USA.Google ScholarCross Ref
- {47} Roobaert D. Improving the Generalization of Linear Support Vector Machines: an Application to 3D Object Recognition with Cluttered Background. Proc. Workshop on Support Vector Machines at the 16th International Joint Conference on Artificial Intelligence, July 31-August 6, Stockholm, Sweden, p. 29-33 1999.Google Scholar
- {48} Scholkopf B., Bartlett P., Smola A. and Williamson R. Support vector regression with automatic accuracy control. In L. Niklasson, M. Boden and T. Ziemke, editors, Proceedings of the 8th International Conference on Artificial Neural Networks, Perspectives in Neural Computing, Berlin, Springer Verlag, 1998.Google Scholar
- {49} Scholkopf B., Bartlett P., Smola A., and Williamson R. Shrinking the Tube: A New Support Vector Regression Algorithm. To appear in: M. S. Kearns, S. A. Solla, and D. A. Cohn (eds.), Advances in Neural Information Processing Systems, 11, MIT Press, Cambridge, MA, 1999. Google ScholarDigital Library
- {50} Scholkopf B., Burges C. and Smola A. Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge, MA. 1998. Google ScholarDigital Library
- {51} Scholkopf B., Platt J. C., Shawe-Taylor J., Smola A. J., Williamson R. C. Estimating the support of a high-dimensional distribution. Microsoft Research Corporation Technical Report MSR-TR-99-87, 1999.Google Scholar
- {52} Scholkopf B., Shawe-Taylor J., Smola A. and Williamson R. Kernel-dependent support vector error bounds. Ninth International Conference on Artificial Neural Networks, IEE Conference Publications No. 470, p. 304-309, 1999.Google ScholarCross Ref
- {53} Scholkopf B., Smola A., and Muller, K.-R., Kernel principal component analysis. In B. Scholkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods: Support Vector Learning. MIT Press, Cambridge, MA, 1999b. 327-352. Google ScholarDigital Library
- {54} Scholkopf B., Smola A., Williamson R., and Bartlett P. New support vector algorithms. To appear in Neural Computation, 1999. Google ScholarDigital Library
- {55} Scholkopf, B., Sung, K., Burges C., Girosi F., Niyogi P., Poggio T. and Vapnik V. Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers. IEEE Transactions on Signal Processing, 45, p. 2758-2765, 1997. Google ScholarDigital Library
- {56} Smola A., Bartlett P., Scholkopf B. and Schuurmans C. (eds), Advances in Large Margin Classifiers, Chapter 2, MIT Press, 1999. Google ScholarDigital Library
- {57} Shawe-Taylor J. and Cristianini N. Margin distribution and soft margin. In A. Smola, P. Barlett, B. Scholkopf and C. Schuurmans (eds), Advances in Large Margin Classifiers, Chapter 2, MIT Press, 1999. Google ScholarDigital Library
- {58} Smola A. and Scholkopf B. A tutorial on support vector regression. NeuroColt2 TR 1998-03, 1998. Google ScholarDigital Library
- {59} Smola A. and Scholkopf B. From Regularization Operators to Support Vector Kernels. In: M. Mozer, M. Jordan, and T. Petsche (eds). Advances in Neural Information Processing Systems, 9, MIT Press, Cambridge, MA, 1997. Google ScholarDigital Library
- {60} Smola A., Scholkopf B. and Muller K.-R. The connection between regulafisation operators and support vector kernels. Neural Networks, 11 p. 637-649, 1998. Google ScholarDigital Library
- {61} Smola A., Williamson R., Mika S., and Scholkopf B. Regularized principal manifolds. In Computational Learning Theory: 4th European Conference, volume 1572 of Lecture Notes in Artificial Intelligence (Springer), p. 214-229, 1999.Google ScholarCross Ref
- {62} Tax D. and Duin R. Data domain description by Support Vectors. In Proceedings of ESANN99, ed. M Verleysen, D. Facto Press, Brussels, p. 251-256, 1999.Google Scholar
- {63} Tax D., Ypma A., and Duin R., Support vector data description applied to machine vibration analysis. In: M. Boasson, J. Kaandorp, J. Tonino, M. Vosselman (eds.), Proc. 5th Annual Conference of the Advanced School for Computing and Imaging (Heijen, NL, June 15-17), 1999, 398-405.Google Scholar
- {64} http://www.ics.uci.edu/mlearn/MLRepository.htmlGoogle Scholar
- {65} Vapnik, V. The Nature of Statistical Learning Theory. Springer, New York, 1995. Google ScholarDigital Library
- {66} Vapnik, V. Statistical Learning Theory. Wiley, 1998. Google ScholarDigital Library
- {67} Weston, J. Gammerman, A., Stitson, M., Vapnik, V., Vovk, V. and Watkins, C. Support Vector Density Estimation. In B. Scholkopf, C. Burges and A. Smola. Advances in Kernel Methods: Support Vector Machines. MIT Press, cambridge, M. A. p. 293-306, 1999. Google ScholarDigital Library
- {68} Vapnik, V. and Chapelle, O. Bounds on error expectation for Support Vector Machines. Submitted to Neural Computation, 1999. Google ScholarDigital Library
- {69} Weston J., Mukherjee, Chapelle, Pontil M., Poggio T., and Vapnik V. Feature Selection for SVMs. To appear in Advances in Neural Information Processing Systems 14 (Morgan Kaufmann, 2001).Google Scholar
- {70} http://kernel-machines.org/Google Scholar
- {71} Zien A., Ratsch G., Mika S., Scholkopf B., Lemmen C., Smola A., Lengauer T. and Muller K.-R. Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites. Presented at the German Conference on Bioinformatics, 1999.Google Scholar
Index Terms
- Support vector machines: hype or hallelujah?
Recommendations
An overview on twin support vector machines
Twin support vector machines (TWSVM) is based on the idea of proximal SVM based on generalized eigenvalues (GEPSVM), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/...
Incremental training of support vector machines using hyperspheres
In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental ...
Twin Support Vector Machines for Pattern Classification
We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized ...
Comments