[1] | Pinar Yanardag and S V N Vishwanathan. Deep graph kernels. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015. (159 out of 819, 19% acceptance rate). [ .pdf ] |
[2] | Pinar Yanardag and S V N Vishwanathan. A structural smoothing framework for robust graph comparison. In C. Cortes, N.D. Lawrence, Daniel D Lee, Masashi Sugiyama, and Roman Garnett, editors, Advances in Neural Information Processing Systems 27, 2015. (403 out of 1838, 21% acceptance rate). |
[3] | Hsiang-Fu Yu, Cho-Jui Hsieh, Hyokyun Yun, S V N Vishwanathan, and Inderjit Dhillon. A scalable asynchronous distributed algorithm for topic modeling. In Proceedings of WWW, 2015. (131 out of 929, 14.1% acceptance rate). [ .pdf ] |
[4] | William Benjamin, Senthil Chandrasegaran, Devarajan Ramanujan, Niklas Elmqvist, S V N Vishwanathan, and Karthik Ramani. Juxtapoze: Supporting serendipity and creative expression in clipart compositions. In ACM CHI Conference on Human Factors in Computing Systems. ACM, 2014. To Appear. (471 out of 2064, 22.8% acceptance rate). [ .pdf ] |
[5] | Joon-Hee Choi and S V N Vishwanathan. DFacTo: Distributed factorization of tensors. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger, editors, Neural Information Processing Systems, pages 1296--1304, 2014. (414 out of 1678, 24.67% acceptance rate). [ .pdf ] |
[6] | Shin Matsushima, Hyokyun Yun, and S V N Vishwanathan. Doubly separable stochastic optimization for regularized empirical risk minimization. In Proceedings of the Very Large Databases (VLDB) Conference, 2014. Submitted. |
[7] | Hyokyun Yun, Parameshwaran Raman, and S V N Vishwanathan. Ranking via robust binary classification and parallel parameter estimation in large-scale data. In Z. Ghahramani, M. Welling, C. Cortes, N.D. Lawrence, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems 27, pages 2582--2590, 2014. (414 out of 1678, 24.67% acceptance rate). [ .pdf ] |
[8] | Hyokyun Yun, Hsiang-Fu Yu, Cho-Jui Hsieh, S V N Vishwanathan, and Inderjit Dhillon. NOMAD: Non-locking, stochastic multi-machine algorithm for asynchronous and decentralized matrix completion. In Proceedings of the Very Large Databases (VLDB) Conference, 2014. [ .pdf ] |
[9] | Feng Yan, Shreyas Sundaram, S V N Vishwanathan, and Yuan Qi. Distributed autonomous online learning: Regrets and intrinsic privacy-preserving properties. IEEE Transactions on Knowledge and Data Engineering, 25(11):2483--2493, November 2013. [ .pdf ] |
[10] | Nan Ding, S V N Vishwanathan, Manfred K. Warmuth, and Vasil Denchev. t-logistic regression. Journal of Machine Learning Research, October 2013. Submitted. |
[11] | Jiazhong Nie, Manfred Warmuth, S V N Vishwanathan, and Xinhua Zhang. Lower bounds for boosting with hadamard matrices. In Elad Hazan, editor, Conference on Learning Theory, 2013. Open Problem. |
[12] | Xinhua Zhang, Ankan Saha, and S V N Vishwanathan. Accelerated training of Max-Margin Markov Networks with kernels. Theoretical Computer Science, 2013. [ .pdf ] |
[13] | Xinhua Zhang, Ankan Saha, and S V N Vishwanathan. Smoothing multivariate performance measures. Journal of Machine Learning Research, 13:3589--3646, December 2012. [ .pdf ] |
[14] | Bharath Hariharan, S V N Vishwanathan, and Manik Varma. Efficient max-margin multi-label classification with applications to zero-shot learning. Machine Learning, 88:127--155, July 2012. [ .pdf ] |
[15] | Vasil Denchev, Nan Ding, S V N Vishwanathan, and Hartmut Neven. Robust classification with adiabatic quantum optimization. In Andrew McCallum, John Langford, Joelle Pineau, Kilian Weinberger, and Amir Globerson, editors, Proceedings of the International Conference on Machine Learning, Edinburgh, Scotland, June 2012. (242 out of 890, 27.1% acceptance rate). [ .pdf ] |
[16] | Hyokun Yun and S V N Vishwanathan. Quilting stochastic Kronecker product graphs to generate multiplicative attribute graphs. In Proceedings of International Workshop on Artificial Intelligence and Statistics, pages 1389--1397, April 2012. (134 out of 400, 33.5% acceptance rate). [ .pdf ] |
[17] | Amr Ahmed, Choon Hui Teo, S V N Vishwanathan, and Alex Smola. Fair and balanced: Learning to present news stories. In Eugene Agichtein and Yoelle Maarek, editors, Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pages 333--342, Seattle,Washington, February 2012. (75 out of 362, 20.7% acceptance rate). [ .pdf ] |
[18] | Asheesh Jain, S V N Vishwanathan, and Manik Varma. Spectral projected gradient descent for efficient and large scale generalized multiple kernel learning. In Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 750--758, 2012. (163 out of 755, 21.5% acceptance rate). [ .pdf ] |
[19] | Shin Matsushima, S V N Vishwanathan, and Alex Smola. Linear support vector machines via dual cached loops. In Eighteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 177--185, 2012. (163 out of 755, 21.5% acceptance rate). [ .pdf ] |
[20] | S V N Vishwanathan. Machine learning. In A.-H. El-Shaarawi and W. Piegorsch, editors, Encyclopedia of Environmetrics, pages 1521--1524. John Wiley and Sons, second edition, 2012. |
[21] | Xinhua Zhang, Ankan Saha, and S V N Vishwanathan. Accelerated training of Max-Margin Markov Networks with kernels. In Jyrki Kivinen and Csaba Szepesvàri, editors, Proceedings of the International Conference on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence, pages 292--307, Espoo, Finland, October 2011. Springer-Verlag. [ .pdf ] |
[22] | William Benjamin, Andrew Wood Polk, S V N Vishwanathan, and Karthik Ramani. Heat walk: Robust salient segmentation of non-rigid shapes. In Yung-Nien Sun, Eugene Fiume, and Ming Ouhyoung, editors, Proceedings of the 19th Pacific Conference on Computer Graphics and Applications, Taiwan, September 2011. Eurographics Association. (27 out of 167, 16% acceptance rate). [ .pdf ] |
[23] | Xinhua Zhang, Ankan Saha, and S V N Vishwanathan. Smoothing multivariate performance measures. In Peter Grünwald, Avi Pfeffer, and Fabio G. Cozman, editors, Proceedings of the Conference on Uncertainty in Artificial Intelligence, pages 814--821, Barcelona, Spain, July 2011. (96 out of 285 34% acceptance rate). [ .pdf ] |
[24] | Yi Fang, S V N Vishwanathan, Mengtian Sun, and Karthik Ramani. sLLE: Spherical locally linear embedding with applications to tomography. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1129--1136, Colorado Springs (USA), June 2011. (438 out of 1677, 26.4% acceptance rate). [ .pdf ] |
[25] | Nan Ding, S V N Vishwanathan, and Alan Qi. t-divergence based approximate inference. In Peter Bartlett, Fernando Pereira, Richard Zemel, John Shawe-Taylor, and Kilian Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 1494--1502, 2011. (305 out of 1400, 21.8% acceptance rate). [ .pdf ] |
[26] | Ankan Saha, S V N Vishwanathan, and Xinhua Zhang. New approximation algorithms for minimum enclosing convex shapes. In Dana Randall, editor, ACM-SIAM Syposium on Discrete Algorithms (SODA), pages 1146--1160, January 2011. (136 out of 454, 30% acceptance rate). [ .pdf ] |
[27] | S V N Vishwanathan, Nicol N. Schraudolph, Imre Risi Kondor, and Karsten M. Borgwardt. Graph kernels. Journal of Machine Learning Research, 11:1201--1242, April 2010. [ .pdf ] |
[28] | Jin Yu, S V N Vishwanathan, Simon Günter, and Nicol N. Schraudolph. A quasi-Newton approach to nonsmooth convex optimization. Journal of Machine Learning Research, 11:1145--1200, March 2010. [ .pdf ] |
[29] | Nan Ding and S V N Vishwanathan. t-logistic regression. In Richard Zemel, John Shawe-Taylor, John Lafferty, Chris Williams, and Alan Culota, editors, Advances in Neural Information Processing Systems 23, pages 514--522, 2010. (293 out of 1219, 24% acceptance rate). [ .pdf ] |
[30] | Bharath Hariharan, Lihi Zelnik-Manor, S V N Vishwanathan, and Manik Varma. Large scale max-margin multi-label classification with priors. In Proceedings of the International Conference on Machine Learning, 2010. (152 out of 594, 25.6% acceptance rate). [ .pdf ] |
[31] | Novi Quadrianto, Alex Smola, Tiberio Caetano, S V N Vishwanathan, and James Petterson. Multitask learning without label correspondences. In Richard Zemel, John Shawe-Taylor, John Lafferty, Chris Williams, and Alan Culota, editors, Advances in Neural Information Processing Systems 23, pages 1957--1965, 2010. (293 out of 1219, 24% acceptance rate). [ .pdf ] |
[32] | Choon Hui Teo, S V N Vishwanthan, Alex J. Smola, and Quoc V. Le. Bundle methods for regularized risk minimization. Journal of Machine Learning Research, 11:311--365, January 2010. [ .pdf ] |
[33] | S V N Vishwanathan, Zhaonan Sun, Nawanol Theera-Ampornpunt, and Manik Varma. Multiple kernel learning and the SMO algorithm. In Richard Zemel, John Shawe-Taylor, John Lafferty, Chris Williams, and Alan Culota, editors, Advances in Neural Information Processing Systems 23, pages 2361--2369, 2010. Poster spotlight. (73 out of 1219, 6% acceptance rate). [ .pdf ] |
[34] | Xinhua Zhang, Ankan Saha, and S V N Vishwanathan. Lower bounds on rate of convergence of cutting plane methods. In Richard Zemel, John Shawe-Taylor, John Lafferty, Chris Williams, and Alan Culota, editors, Advances in Neural Information Processing Systems 23, pages 2541--2549, 2010. (293 out of 1219, 24% acceptance rate). [ .pdf ] |
[35] | Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, and S V N Vishwanathan. Hash kernels for structured data. Journal of Machine Learning Research, 10:2615--2637, November 2009. [ .pdf ] |
[36] | Jin Yu, S V N Vishwanathan, and Jian Zhang. The entire quantile path of a risk-agnostic SVM classifier. In David McAllester, Jeff Blimes, and Andrew Ng, editors, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), Montreal, Canada, June 2009. (76 out of 243, 31% acceptance rate). [ .pdf ] |
[37] | Nino Shervashidze, S V N Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten Borgwardt. Efficient graphlet kernels for large graph comparison. In Max Welling and David van Dyk, editors, Proceedings of International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate). [ .pdf ] |
[38] | Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola, Alex Strehl, and S V N Vishwanathan. Hash kernels. In Max Welling and David van Dyk, editors, Proceedings of International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate). [ .pdf ] |
[39] | Peter Sunehag, Jochen Trumpf, S V N Vishwanathan, and Nicol N. Schraudolph. Variable metric stochastic approximation theory. In Max Welling and David van Dyk, editors, Proceedings of International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2009. (84 out of 210, 40% acceptance rate). [ .pdf ] |
[40] | Manfred K. Warmuth, Karen A. Glocer, and S V N Vishwanathan. Entropy regularized LPBoost. In Yoav Freund, Yoav Làszlò Györfi, and György Turàn, editors, Proceedings of the International Conference on Algorithmic Learning Theory, number 5254 in Lecture Notes in Artificial Intelligence, pages 256--271, Budapest, October 2008. Springer-Verlag. [ .pdf ] |
[41] | Jin Yu, S V N Vishwanathan, Simon Günter, and Nicol N. Schraudolph. A quasi-Newton approach to nonsmooth convex optimization. In Andrew McCallum and Sam Roweis, editors, Proceedings of the International Conference on Machine Learning, Helsinki, Finland, July 2008. (155 out of 583, 26.5% acceptance rate). [ .pdf ] |
[42] | Li Cheng, S V N Vishwanathan, and Xinhua Zhang. Consistent image analogies using semi-supervised learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska (USA), June 2008. IEEE Computer Society. (508 out of 1593, 32% acceptance rate). [ .pdf ] |
[43] | Simon Günter, Nicol N. Schraudolph, and S V N Vishwanathan. Fast iterative kernel principal component analysis. Journal of Machine Learning Research, 8:1893--1918, August 2007. [ .pdf ] |
[44] | Choon Hui Teo, Quoc V. Le, Alexander J. Smola, and S V N Vishwanathan. A scalable modular convex solver for regularized risk minimization. In Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2007. (100 out of 500, 20% acceptance rate). [ .pdf ] |
[45] | Li Cheng and S V N Vishwanathan. Learning to compress images and video. In Proceedings of the International Conference on Machine Learning, pages 161--168, June 2007. (152 out of 522, 29% acceptance rate). [ .pdf ] |
[46] | S V N Vishwanathan, Alexander J. Smola, and René Vidal. Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes. International Journal of Computer Vision, 73(1):95--119, June 2007. [ .pdf ] |
[47] | Xinhua Zhang, Douglas Aberdeen, and S V N Vishwanathan. Conditional random fields for multi-agent reinforcement learning. In Proceedings of the International Conference on Machine Learning, pages 1143--1150, June 2007. best student paper award, (152 out of 522, 29% acceptance rate). [ .pdf ] |
[48] | Gökhan Bakir, Thomas Hofmann, Bernhard Schölkopf, Alexander J. Smola, Ben Taskar, and S V N Vishwanathan, editors. Predicting Structured Data. MIT Press, Cambridge, Massachusetts, 2007. |
[49] | Karsten M. Borgwardt, Hans-Peter Kriegel, S V N Vishwanathan, and Nicol N. Schraudolph. Graph kernels for disease outcome prediction from protein-protein interaction networks. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E Klein, editors, Proceedings of the Pacific Symposium of Biocomputing 2007, Maui Hawaii, January 2007. World Scientific. [ .pdf ] |
[50] | Qinfeng Shi, Yasemin Altun, Alexander J. Smola, and S V N Vishwanathan. Semi-Markov models for sequence segmentation. In Proceedings of the 2007 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 640--648, 2007. (66 out of 398, 16.5% acceptance rate). [ .pdf ] |
[51] | Alexander J. Smola, S V N Vishwanathan, and Quoc V. Le. Bundle methods for machine learning. In John Platt, Daphne Koller, Yoram Singer, and Sam Roweis, editors, Advances in Neural Information Processing Systems 20, Cambridge MA, 2007. MIT Press. (217 out of 975, 22% acceptance rate). [ .pdf ] |
[52] | S V N Vishwanathan, Nicol N. Schraudolph, and Alexander J. Smola. Step size adaptation in reproducing kernel Hilbert space. Journal of Machine Learning Research, 7:1107--1133, June 2006. [ .pdf ] |
[53] | Karsten M. Borgwardt, S V N Vishwanathan, and Hans-Peter Kriegel. Class prediction from time series gene expression profiles using dynamical systems kernels. In Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Tiffany Murray, and Teri E Klein, editors, Proceedings of the Pacific Symposium of Biocomputing 2006, pages 547--558, Maui Hawaii, January 2006. World Scientific. [ .pdf ] |
[54] | Li Cheng, S V N Vishwanathan, Dale Schuurmans, Shaojun Wang, and Terry Caelli. Implicit online learning with kernels. In Bernhard Schölkopf, John Platt, and Thomas Hofmann, editors, Advances in Neural Information Processing Systems 19, Cambridge MA, 2006. MIT Press. (204 out of 833, 24.5% acceptance rate). [ .pdf ] |
[55] | Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, and S V N Vishwanathan. Large-scale multiclass transduction. In Yair Weiss, Bernhard Schölkopf, and John Platt, editors, Advances in Neural Information Processing Systems 18, pages 411--418, Cambride, MA, 2006. MIT Press. (206 out of 753, 27.5% acceptance rate). [ .pdf ] |
[56] | Nicol N. Schraudolph, Simon Günter, and S V N Vishwanathan. Fast iterative kernel PCA. In Bernhard Schölkopf, John Platt, and Thomas Hofmann, editors, Advances in Neural Information Processing Systems 19, Cambridge MA, 2006. MIT Press. (204 out of 833, 24.5% acceptance rate). [ .pdf ] |
[57] | Choon Hui Teo and S V N Vishwanathan. Fast and space efficient string kernels using suffix arrays. In Proceedings of the International Conference on Machine Learning, pages 929--936, New York, NY, USA, 2006. ACM Press. (140 out of 700, 20% acceptance rate). [ .pdf ] |
[58] | S V N Vishwanathan, Karsten M. Borgwardt, Omri Guttman, and Alexander J. Smola. Kernel extrapolation. Neurocomputing, 69(7-9):721--729, 2006. [ .pdf ] |
[59] | S V N Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph. Fast computation of graph kernels. In Bernhard Schölkopf, John Platt, and Thomas Hofmann, editors, Advances in Neural Information Processing Systems 19, Cambridge MA, 2006. MIT Press. (204 out of 833, 24.5% acceptance rate). [ .pdf ] |
[60] | S V N Vishwanathan, Nicol N. Schraudolph, Mark Schmidt, and Kevin Murphy. Accelerated training of conditional random fields with stochastic gradient methods. In Proceedings of the International Conference on Machine Learning, pages 969--976, New York, NY, USA, 2006. ACM Press. (140 out of 700, 20% acceptance rate). [ .pdf ] |
[61] | Omri Guttman, S V N Vishwanathan, and Robert C. Williamson. Probabilistic automata learning via oracles. In Sanjay Jain, Hans Ulrich Simon, and Etsuji Tomita, editors, Proceedings of the International Conference on Algorithmic Learning Theory, number 3734 in Lecture Notes in Artificial Intelligence, pages 171--182, Singapore, October 2005. Springer-Verlag. (30 out of 98, 30% acceptance rate). [ .pdf ] |
[62] | A. Karatzoglou, S V N Vishwanathan, Nicol N. Schraudolph, and Alexander J. Smola. Step size-adapted online support vector learning. In Proceedings of ISSPA 2005, Australia, August 2005. [ .pdf ] |
[63] | Manfred K. Warmuth and S V N Vishwanathan. Leaving the span. In P. Auer and R. Meir, editors, Proceedings of the Annual Conference on Computational Learning Theory, number 3559 in Lecture Notes in Artificial Intelligence, pages 365--380, Bertinoro, Italy, June 2005. Springer-Verlag. (45 out of 120, 37.5% acceptance rate). [ .pdf ] |
[64] | Karsten M. Borgwardt, Omri Guttman, S V N Vishwanathan, and Alexander J. Smola. Joint regularization. In Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2005), Brugge, Belgium, 2005. [ .pdf ] |
[65] | Karsten M. Borgwardt, C. S. Ong, S. Schönauer, S V N Vishwanathan, Alexander J. Smola, and Hans-Peter Kriegel. Protein function prediction via graph kernels. In Proceedings of Intelligent Systems in Molecular Biology (ISMB), Detroit, USA, 2005. (13% acceptance rate). [ .pdf ] |
[66] | Gaelle Loosli, Stephané Canu, S V N Vishwanathan, and Alexander J. Smola. Invariances in classification: an efficient SVM implementation. In ASMDA 2005 - Applied Stochastic Models and Data Analysis, 2005. |
[67] | Gaelle Loosli, Stephané Canu, S V N Vishwanathan, Alexander J. Smola, and Manojit Chattopadhyay. Boîte á outils SVM simple et rapide. RIA - Revue d'intelligence artificielle, 2005. |
[68] | Alexander J. Smola, S V N Vishwanathan, and Thomas Hofmann. Kernel methods for missing variables. In R.G. Cowell and Z. Ghahramani, editors, Proceedings of International Workshop on Artificial Intelligence and Statistics, pages 325--332. Society for Artificial Intelligence and Statistics, 2005. (21 out of 150, 14% acceptance rate). [ .pdf ] |
[69] | S V N Vishwanathan and Alexander J. Smola. Binet-Cauchy kernels. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1441--1448, Cambridge, MA, 2005. MIT Press. (207 out of 822, 25% acceptance rate). [ .pdf ] |
[70] | Gaelle Loosli, Stephané Canu, S V N Vishwanathan, Alexander J. Smola, and Manojit Chattopadhyay. Une boîte á outils rapide et simple pour les SVM. In Michel Liquiére and Marc Sebban, editors, CAp 2004 - Conférence d'Apprentissage, pages 113--128. Presses Universitaires de Grenoble, 2004. |
[71] | Alexander J. Smola, S V N Vishwanathan, and Eleazar Eskin. Laplace propogation. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems 16, pages 441--448, Cambridge, MA, 2004. MIT Press. (198 out of 717, 27.5% acceptance rate). [ .pdf ] |
[72] | S V N Vishwanathan and Alexander J. Smola. Fast kernels for string and tree matching. In B. Schölkopf, K. Tsuda, and J.P. Vert, editors, Kernel Methods in Computational Biology, Cambridge, MA, 2004. MIT Press. [ .pdf ] |
[73] | Alexander J. Smola and S V N Vishwanathan. Hilbert space embeddings in dynamical systems. In Proceedings of the 13th IFAC symposium on system identification, Rotterdam, Netherlands, August 2003. [ .pdf ] |
[74] | S V N Vishwanathan and Alexander J. Smola. Fast kernels for string and tree matching. In S. Becker, Sebastian Thrun, and Klaus Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 569--576. MIT Press, Cambridge, MA, 2003. (221 out of 710, 31.1% acceptance rate). [ .pdf ] |
[75] | S V N Vishwanathan, Alexander J. Smola, and M. N. Murty. SimpleSVM. In Tom Fawcett and Nina Mishra, editors, Proceedings of the International Conference on Machine Learning, pages 760--767, Washington DC, 2003. AAAI press. (119 out of 371, 32% acceptance rate). [ .pdf ] |
[76] | S V N Vishwanathan and M. N. Murty. Use of MPSVM for data set reduction. In A. Abraham, L. Jain, and J. Kacprzyk, editors, Recent Advances in Intelligent Paradigms and Applications, volume 113 of Studies in Fuzziness and Soft Computing, chapter 16. Springer Verlag, Berlin, November 2002. [ .pdf ] |
[77] | S V N Vishwanathan and M. N. Murty. Jigsawing: A method to generate virtual examples in OCR data. In Hybrid Intelligent Systems, 2002. [ .pdf ] |
[78] | S V N Vishwanathan and M. N. Murty. SSVM: A simple SVM algorithm. In C. Lee Giles, editor, Proc. Intl. Joint Conf. on Neural Networks. IEEE Press, 2002. [ .pdf ] |
[79] | S V N Vishwanathan and M. N. Murty. Geometric SVM: A fast and intuitive SVM algorithm. In Proc. Intl. Conf. Pattern Recognition, 2002. [ .pdf ] |
[80] | S V N Vishwanathan and M. N. Murty. Use of MPSVM for data set reduction. In A. Abraham and M. Koeppen, editors, Hybrid Information Systems, Heidelberg, 2001. Physica Verlag. [ .pdf ] |
[81] | S V N Vishwanathan and M. N. Murty. Kohonen's SOM with cache. Pattern Recognition, 33(11):1927--1929, November 2000. [ .pdf ] |
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