
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
Tsuyoshi Kato, Hisahi Kashima, Masashi Sugiyama, Kiyoshi Asai, "Conic Programming for Multitask Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 957968, July, 2010.  
BibTex  x  
@article{ 10.1109/TKDE.2009.142, author = {Tsuyoshi Kato and Hisahi Kashima and Masashi Sugiyama and Kiyoshi Asai}, title = {Conic Programming for Multitask Learning}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {22}, number = {7}, issn = {10414347}, year = {2010}, pages = {957968}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.142}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Conic Programming for Multitask Learning IS  7 SN  10414347 SP957 EP968 EPD  957968 A1  Tsuyoshi Kato, A1  Hisahi Kashima, A1  Masashi Sugiyama, A1  Kiyoshi Asai, PY  2010 KW  Multitask learning KW  secondorder cone programming KW  ordinal regression KW  link prediction KW  collaborative filtering. VL  22 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] Y. Amit, M. Fink, N. Srebro, and S. Ullman, "Uncovering Shared Structures in Multiclass Calssification," Proc. 24th Int'l Conf. Machine Learning, pp. 1724, 2007.
[2] R. Caruana, "Multitask Learning," Machine Learning, vol. 28, no. 1, pp. 4175, 1997.
[3] S. Thrun and L. Pratt, Learning to Learn. Springer, 1997.
[4] J. Baxter, "A Model of Inductive Bias Learning," J. Artificial Intelligence Research, vol. 12, pp. 149198, 2000.
[5] B. Bakker and T. Heskes, "Task Clustering and Gating for Bayesian Multitask Learning," J. Machine Learning Research, vol. 4, pp. 8399, 2003.
[6] T. Evgeniou and M. Pontil, "Regularized Multitask Learning," Proc. ACM SIGKDD, pp. 109117, 2004.
[7] T. Evgeniou, C.A. Micchelli, and M. Pontil, "Learning Multiple Tasks with Kernel Methods," J. Machine Learning Research, vol. 6, pp. 615637, 2005.
[8] N.D. Lawrence and J.C. Platt, "Learning to Learn with the Informative Vector Machine," Proc. 21st Int'l Conf. Machine Learning, pp. 512519, 2004.
[9] C.A. Micchelli and M. Pontil, "Kernels for MultiTask Learning," Advances in Neural Information Processing Systems, vol. 17, pp. 921928, MIT Press, 2005.
[10] K. Yu, V. Tresp, and A. Schwaighofer, "Learning Gaussian Processes from Multiple Tasks," Proc. 22nd Int'l Conf. Machine Learning, pp. 10121019, 2005.
[11] E.V. Bonilla, F.V. Agakov, and C.K.I. Williams, "Kernel MultiTask Learning Using TaskSpecific Features," Proc. 11th Int'l Conf. Artificial Intelligence and Statistics, pp. 4350, 2007.
[12] K. Tsuda and W.S. Noble, "Learning Kernels from Biological Networks by Maximizing Entropy," Bioinformatics, vol. 20, no. suppl. 1, pp. i326i333, 2004.
[13] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004.
[14] A. Shashua and A. Levin, "Ranking with Large Margin Principle: Two Approaches," Advances in Neural Information Processing Systems, vol. 15, pp. 937944, MIT Press, 2003.
[15] T. Kato, K. Tsuda, and K. Asai, "Selective Integration of Multiple Biological Data for Supervised Network Inference," Bioinformatics, vol. 21, pp. 24882495, 2005.
[16] J.P. Vert and Y. Yamanishi, "Supervised Graph Inference," Advances in Neural Information Processing Systems, vol. 17. MIT Press, 2005.
[17] Y. Yamanishi, J.P. Vert, and M. Kanehisa, "Supervised Enzyme Network Inference from the Integration of Genomic Data and Chemical Information," Bioinformatics, vol. 21, suppl. 1, pp. i468i477, June 2005.
[18] K. Bleakley, G. Biau, and J.P. Vert, "Supervised Reconstruction of Biological Networks with Local Models," Bioinformatics, vol. 23, no. 13, pp. i57i65, 2007.
[19] S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman, "Indexing by Latent Semantic Analysis," J. Am. Soc. for Information Science, vol. 41, no. 6, pp. 391407, 1990.
[20] Y. Xue, X. Liao, L. Carin, and B. Krishnapuram, "MultiTask Learning for Classification with Dirichlet Process Priors," J. Machine Learning Research, vol. 8, pp. 3563, 2007.
[21] V.N. Vapnik, Statistical Learning Theory. Wiley, 1998.
[22] B. Borchers, "CSDP, a C Library for Semidefinite Programming," Optimization Methods and Software, vol. 11, no. 1, pp. 613623, 1999.
[23] M. Lobo, L. Vandenberghe, S. Boyd, and H. Lebret, "Applications of SecondOrder Cone Programming," Linear Algebra and its Applications, vol. 284, pp. 193228, 1998.
[24] X. Zhu, J. Kandola, Z. Ghahramani, and J. Lafferty, "Nonparametric Transforms of Graph Kernels for SemiSupervised Learning," Advances in Neural Information Processing Systems, vol. 17, pp. 16411648, MIT Press, 2004.
[25] D. Haussler, "Convolution Kernels on Discrete Structures," Technical Report UCSCCRL9910, UC Santa Cruz, July 1999.
[26] T. Jaakkola and D. Haussler, "Exploiting Generative Models in Discriminative Classifiers," Advances in Neural Information Processing Systems, M.S. Kearns, S.A. Solla, and D.A. Cohn, eds., vol. 11, pp. 487493, MIT Press, 1999.
[27] H. Lodhi, C. Saunders, J. ShaweTaylor, N. Cristianini, and C. Watkins, "Text Classification Using String Kernels," J. Machine Learning Research, vol. 2, pp. 419444, 2002.
[28] R.I. Kondor and J. Lafferty, "Diffusion Kernels on Graphs and Other Discrete Input Spaces," Proc. 19th Int'l Conf. Machine Learning, pp. 315322, 2002.
[29] C. Leslie, E. Eskin, and W.S. Noble, "The Spectrum Kernel: A String Kernel for SVM Protein Classification," Proc. Pacific Symp. Biocomputing, pp. 566575, 2002.
[30] H. Kashima and T. Koyanagi, "Kernels for SemiStructured Data," Proc. 19th Int'l Conf. Machine Learning, pp. 291298, 2002.
[31] T. Gärtner, "A Survey of Kernels for Structured Data," SIGKDD Explorations, vol. 5, no. 1, pp. S268S275, 2003.
[32] T. Gärtner, P. Flach, and S. Wrobel, "On Graph Kernels: Hardness Results and Efficient Alternatives," Proc. 16th Ann. Conf. Computational Learning Theory, pp. 129143, 2003.
[33] H. Kashima, K. Tsuda, and A. Inokuchi, "Marginalized Kernels between Labeled Graphs," Proc. 20th Int'l Conf. Machine Learning, pp. 321328, 2003.
[34] R. Milo, S. ShenOrr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, "Network Motifs: Simple Building Blocks of Complex Networks," Science, vol. 298, pp. 824827, Jan. 2002.
[35] A. Andreeva, D. Howorth, S.E. Brenner, T.J.P. Hubbard, C. Chothia, and A.G. Murzin, "SCOP Database in 2004: Refinements Integrate Structure and Sequence Family Data," Nuclear Acid Research, vol. 32, pp. D226D229, 2004.
[36] E.L. Lehmann and J.P. Romano, Testing Statistical Hypotheses. Springer, 2005.
[37] C. von Mering, R. Krause, B. Snel, M. Cornell, S.G. Oliver, S. Fields, and P. Bork, "Comparative Assessment of LargeScale Data Sets of ProteinProtein Interactions," Nature, vol. 417, pp. 399403, 2002.
[38] G.R.G. Lanckriet, T.D. Bie, N. Cristianini, M.I. Jordan, and W.S. Noble, "A Statistical Framework for Genomic Data Fusion," Bioinformatics, vol. 20, pp. 26262635, 2004.
[39] M. Kurucz, A.A. Benczúr, T. Kiss, I. Nagy, A. Szabó, and B. Torma, "KDD Cup 2007 Task1 Winner Report," ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, pp. 5356, 2008.
[40] N. Srebro, J.D.M. Rennie, and T.S. Jaakkola, "MaximumMargin Matrix Factorization," Advances in Neural Information Processing Systems, L. Saul, Y. Weiss, and L. Bottou, eds., vol. 17, pp. 13291336, MIT Press, 2005.
[41] E. Bonilla, K.M. Chai, and C. Williams, "MultiTask Gaussian Process Prediction" Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis, eds., vol. 20, pp. 153160, MIT Press, 2008.
[42] X. Liao, Y. Xue, and L. Carin, "Logistic Regression with an Auxiliary Data Source," Proc. 22nd Int'l Conf. Machine Learning, pp. 505512, 2005.
[43] Q. Liu, X. Liao, and L. Carin, "SemiSupervised Multitask Learning," Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis, eds., vol. 20, pp. 937944, MIT Press, 2008.
[44] B. Schölkopf, J.C. Platt, J. ShaweTaylor, A.J. Smola, and R.C. Williamson, "Estimating the Support of a HighDimensional Distribution," Neural Computation, vol. 13, no. 7, pp. 14431471, 2001.
[45] D.M.J. Tax and R.P.W. Duin, "Support Vector Data Description," Machine Learning, vol. 54, no. 1, pp. 4566, 2004.
[46] H. Drucker, C.J.C. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support Vector Regression Machines," Advances in Neural Information Processing Systems, M. C. Mozer, M. I. Jordan, and T. Petsche, eds., vol. 9, pp. 155161, MIT Press, 1997.
[47] C.C. Chang and C.J. Lin, "Training νSupport Vector Regression: Theory and Algorithms," Neural Computation, vol. 14, no. 8, pp. 19591977, 2002.
[48] B. Schölkopf, A. Smola, R. Williamson, and P. Bartlett, "New Support Vector Algorithms," Neural Computation, vol. 12, no. 5, pp. 12071245, 2000.
[49] F. PerezCruz, D.J.L.H.J. Weston, and B. Schölkopf, "Extension of the νSVM Range for Classification," Advances in Learning Theory: Methods, Models and Applications, J.A.K. Suykens, G. Horvath, S. Basu, C. Micchelli, and J. Vandewalle, eds., vol. 190, pp. 179196, IOS Press, 2003,
[50] P.H. Chen, C.J. Lin, and B. Schölkopf, "A Tutorial on νSupport Vector Zmachines," Applied Stochastic Models in Business and Industry, vol. 21, no. 2, pp. 111136, 2005.
[51] B. Efron, T. Hastie, R. Tibshirani, and I. Johnstone, "Least Angle Regression," The Annals of Statistics, vol. 32, no. 2, pp. 407499, 2004.
[52] T. Hastie, S. Rosset, R. Tibshirani, and J. Zhu, "The Entire Regularization Path for the Support Vector Machine," J. Machine Learning Research, vol. 5, pp. 13911415, 2004.
[53] G.R.G. Lanckriet, N. Cristianini, P. Bartlett, L.E. Ghaoui, and M.I. Jordan, "Learning the Kernel Matrix with Semidefinite Programming," J. Machine Learning Research, vol. 5, pp. 2772, Jan. 2004.
[54] T. Kato, H. Kashima, and M. Sugiyama, "Integration of Multiple Networks for Robust Label Propagation," Proc. 2008 SIAM Int'l Conf. Data Mining (SDM '08), pp. 716726, 2008.