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Volumn , Issue , 2007, Pages 727-736

A scalable modular convex solver for regularized risk minimization

Author keywords

Algorithms; Convexity; Optimization

Indexed keywords

ALGORITHMS; CLUSTER ANALYSIS; FUNCTIONAL ANALYSIS; OPTIMIZATION; PROBLEM SOLVING; REGRESSION ANALYSIS;

EID: 36849059715     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1281192.1281270     Document Type: Conference Paper
Times cited : (118)

References (43)
  • 4
    • 0026860799 scopus 로고
    • Robust linear programming discrimination of two linearly inseparable sets
    • K. P. Bennett and O. L. Mangasarian. Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Softw., 1:23-34, 1992.
    • (1992) Optim. Methods Softw , vol.1 , pp. 23-34
    • Bennett, K.P.1    Mangasarian, O.L.2
  • 5
    • 36849023213 scopus 로고    scopus 로고
    • S. Benson, L. Curfman-McInnes, J. Moré, and J. Sarich. TAO user manual. Technical Report ANL/MCS-TM-242, Argonne National Laboratory, 2004.
    • S. Benson, L. Curfman-McInnes, J. Moré, and J. Sarich. TAO user manual. Technical Report ANL/MCS-TM-242, Argonne National Laboratory, 2004.
  • 6
    • 0000732463 scopus 로고
    • A limited memory algorithm for bound constrained optimization
    • R. Byrd, P. Lu, J. Nocedal, and C. Zhu. A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5):1190-1208, 1995.
    • (1995) SIAM Journal on Scientific Computing , vol.16 , Issue.5 , pp. 1190-1208
    • Byrd, R.1    Lu, P.2    Nocedal, J.3    Zhu, C.4
  • 7
    • 18744367558 scopus 로고    scopus 로고
    • Hierarchical document categorization with support vector machines
    • New York, NY, USA, ACM Press
    • L. Cai and T. Hofmann. Hierarchical document categorization with support vector machines. In Proc. of ACM conference on info. and knowledge mgmt., pages 78-87, New York, NY, USA, 2004. ACM Press.
    • (2004) Proc. of ACM conference on info. and knowledge mgmt , pp. 78-87
    • Cai, L.1    Hofmann, T.2
  • 8
    • 29144439194 scopus 로고    scopus 로고
    • Decoding by linear programming
    • B. Candes and T. Tao. Decoding by linear programming. IEEE Trans. Info Theory, 51(12):4203-4215, 2005.
    • (2005) IEEE Trans. Info Theory , vol.51 , Issue.12 , pp. 4203-4215
    • Candes, B.1    Tao, T.2
  • 10
    • 33749246680 scopus 로고    scopus 로고
    • Training a support vector machine in the primal
    • Max Planck Institute for Biological Cybernetics
    • O. Chapelle. Training a support vector machine in the primal. Technical Report TR. 147, Max Planck Institute for Biological Cybernetics, 2006.
    • (2006) Technical Report TR , vol.147
    • Chapelle, O.1
  • 13
    • 0001087620 scopus 로고    scopus 로고
    • Logistic regression, AdaBoost and Bregman distances
    • Morgan Kaufmann, San Francisco
    • M. Collins, R. B. Schapire, and Y. Singer. Logistic regression, AdaBoost and Bregman distances. In COLT, pages 158-169. Morgan Kaufmann, San Francisco, 2000.
    • (2000) COLT , pp. 158-169
    • Collins, M.1    Schapire, R.B.2    Singer, Y.3
  • 15
    • 14544278410 scopus 로고    scopus 로고
    • Online ranking by projecting
    • K. Crammer and Y. Singer. Online ranking by projecting. Neural Computation, 17(1):145-175, 2005.
    • (2005) Neural Computation , vol.17 , Issue.1 , pp. 145-175
    • Crammer, K.1    Singer, Y.2
  • 18
    • 0141692489 scopus 로고    scopus 로고
    • Efficient SVM training using low-rank kernel representation
    • Technical report, IBM Watson Research Center, New York
    • S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representation. Technical report, IBM Watson Research Center, New York, 2000.
    • (2000)
    • Fine, S.1    Scheinberg, K.2
  • 19
    • 0041494125 scopus 로고    scopus 로고
    • Efficient SVM training using low-rank kernel representations
    • S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representations. JMLR, 2001.
    • (2001) JMLR
    • Fine, S.1    Scheinberg, K.2
  • 20
    • 84898954071 scopus 로고    scopus 로고
    • Linear hinge loss and average margin
    • Cambridge, MA
    • C. Gentile and M. K. Warmuth. Linear hinge loss and average margin. In NIPS 11, pages 225-231, Cambridge, MA, 1999.
    • (1999) NIPS 11 , pp. 225-231
    • Gentile, C.1    Warmuth, M.K.2
  • 21
    • 0008371352 scopus 로고    scopus 로고
    • Large margin rank boundaries for ordinal regression
    • A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Cambridge, MA, MIT Press
    • R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, pages 115-132, Cambridge, MA, 2000. MIT Press.
    • (2000) Advances in Large Margin Classifiers , pp. 115-132
    • Herbrich, R.1    Graepel, T.2    Obermayer, K.3
  • 23
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale SVM learning practical
    • B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Cambridge, MA, MIT Press
    • T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
    • (1999) Advances in Kernel Methods - Support Vector Learning , pp. 169-184
    • Joachims, T.1
  • 24
    • 31844446804 scopus 로고    scopus 로고
    • A support vector method for multivariate performance measures
    • San Francisco, California, Morgan Kaufmann Publishers
    • T. Joachims. A support vector method for multivariate performance measures. In ICML, pages 377-384, San Francisco, California, 2005. Morgan Kaufmann Publishers.
    • (2005) ICML , pp. 377-384
    • Joachims, T.1
  • 25
    • 33749563073 scopus 로고    scopus 로고
    • T. Joachims. Training linear SVMs in linear time. In KDD, 2006.
    • T. Joachims. Training linear SVMs in linear time. In KDD, 2006.
  • 26
    • 21844461582 scopus 로고    scopus 로고
    • A modified finite Newton method for fast solution of large scale linear SVMs
    • S. S. Keerthi and D. DeCoste. A modified finite Newton method for fast solution of large scale linear SVMs. JMLR, 6:341-361, 2005.
    • (2005) JMLR , vol.6 , pp. 341-361
    • Keerthi, S.S.1    DeCoste, D.2
  • 28
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic modeling for segmenting and labeling sequence data
    • J. D. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic modeling for segmenting and labeling sequence data. In ICML, volume 18, pages 282-289, 2001.
    • (2001) ICML , vol.18 , pp. 282-289
    • Lafferty, J.D.1    McCallum, A.2    Pereira, F.3
  • 29
    • 79952602419 scopus 로고    scopus 로고
    • Direct optimization of ranking measures
    • submitted
    • Q. Le and A. Smola. Direct optimization of ranking measures. JMLR, 2007. submitted.
    • (2007) JMLR
    • Le, Q.1    Smola, A.2
  • 30
    • 0000963583 scopus 로고
    • Linear and nonlinear separation of patterns by linear programming
    • O. L. Mangasarian. Linear and nonlinear separation of patterns by linear programming. Oper. Res., 13:444-452, 1965.
    • (1965) Oper. Res , vol.13 , pp. 444-452
    • Mangasarian, O.L.1
  • 31
    • 84956628443 scopus 로고    scopus 로고
    • K.-R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. Predicting time series with support vector machines. In ICANN'97, pages 999-1004, 1997.
    • K.-R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik. Predicting time series with support vector machines. In ICANN'97, pages 999-1004, 1997.
  • 32
    • 36849004677 scopus 로고    scopus 로고
    • B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. TR 87, Microsoft Research, Redmond, WA, 1999.
    • B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. TR 87, Microsoft Research, Redmond, WA, 1999.
  • 34
    • 85043116988 scopus 로고    scopus 로고
    • Shallow parsing with conditional random fields
    • F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proceedings of HLT-NAACL, pages 213-220, 2003.
    • (2003) Proceedings of HLT-NAACL , pp. 213-220
    • Sha, F.1    Pereira, F.2
  • 35
    • 36849029369 scopus 로고    scopus 로고
    • Online learning meets optimization in the dual
    • extended version
    • S. Shalev-Shwartz and Y. Singer. Online learning meets optimization in the dual. In COLT, 2006. extended version.
    • (2006) COLT
    • Shalev-Shwartz, S.1    Singer, Y.2
  • 36
    • 33750373672 scopus 로고    scopus 로고
    • Large scale semi-supervised linear svms
    • New York, NY, USA, ACM Press
    • V. Sindhwani and S. Keerthi. Large scale semi-supervised linear svms. In SIGIR '06, pages 477-484, New York, NY, USA, 2006. ACM Press.
    • (2006) SIGIR '06 , pp. 477-484
    • Sindhwani, V.1    Keerthi, S.2
  • 38
    • 84898948585 scopus 로고    scopus 로고
    • Max-margin Markov networks
    • B. Taskar, C. Guestrin, and D. Koller. Max-margin Markov networks. In NIPS, pages 25-32, 2004.
    • (2004) NIPS , pp. 25-32
    • Taskar, B.1    Guestrin, C.2    Koller, D.3
  • 39
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol., 58:267-288, 1996.
    • (1996) J. R. Stat. Soc. Ser. B Stat. Methodol , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 40
    • 24944537843 scopus 로고    scopus 로고
    • Large margin methods for structured and interdependent output variables
    • I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. JMLR, 6:1453-1484, 2005.
    • (2005) JMLR , vol.6 , pp. 1453-1484
    • Tsochantaridis, I.1    Joachims, T.2    Hofmann, T.3    Altun, Y.4
  • 41
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • V. Vapnik, S. Golowich, and A. J. Smola. Support vector method for function approximation, regression estimation, and signal processing. In NIPS, pages 281-287, 1997.
    • (1997) NIPS , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.J.3
  • 42
    • 84898962121 scopus 로고    scopus 로고
    • Fast kernels for string and tree matching
    • S. V. N. Vishwanathan and A. J. Smola. Fast kernels for string and tree matching. In NIPS, pages 569-576, 2003.
    • (2003) NIPS , pp. 569-576
    • Vishwanathan, S.V.N.1    Smola, A.J.2
  • 43
    • 0003017575 scopus 로고    scopus 로고
    • Prediction with Gaussian processes: From linear regression to linear prediction and beyond
    • M. I. Jordan, editor, Kluwer Academic
    • C. K. I. Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning and Inference in Graphical Models, pages 599-621. Kluwer Academic, 1998.
    • (1998) Learning and Inference in Graphical Models , pp. 599-621
    • Williams, C.K.I.1


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