-
1
-
-
0038368335
-
Stability and generalization
-
Mar.
-
O. Bousquet and A. Elisseeff, "Stability and generalization, " J. Mach. Learn. Res., vol. 2, pp. 499-526, Mar. 2002.
-
(2002)
J. Mach. Learn. Res.
, vol.2
, pp. 499-526
-
-
Bousquet, O.1
Elisseeff, A.2
-
2
-
-
1842420581
-
General conditions for predictivity in learning theory
-
T. Poggio, R. Rifkin, S. Mukherjee, and P. Niyogi, "General conditions for predictivity in learning theory, " Nature, vol. 428, no. 6981, pp. 419-422, 2004.
-
(2004)
Nature
, vol.428
, Issue.6981
, pp. 419-422
-
-
Poggio, T.1
Rifkin, R.2
Mukherjee, S.3
Niyogi, P.4
-
3
-
-
33745655665
-
Learning theory: Stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
-
S. Mukherjee, P. Niyogi, T. Poggio, and R. Rifkin, "Learning theory: Stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization, " Adv. Comput. Math., vol. 25, no. 1, pp. 161-193, 2006.
-
(2006)
Adv. Comput. Math.
, vol.25
, Issue.1
, pp. 161-193
-
-
Mukherjee, S.1
Niyogi, P.2
Poggio, T.3
Rifkin, R.4
-
5
-
-
0036913330
-
A PAC-Bayesian margin bound for linear classifiers
-
Dec.
-
R. Herbrich and T. Graepel, "A PAC-Bayesian margin bound for linear classifiers, " IEEE Trans. Inf. Theory, vol. 48, no. 12, pp. 3140-3150, Dec. 2002.
-
(2002)
IEEE Trans. Inf. Theory
, vol.48
, Issue.12
, pp. 3140-3150
-
-
Herbrich, R.1
Graepel, T.2
-
6
-
-
0037399538
-
PAC-Bayesian stochastic model selection
-
D. A. McAllester, "PAC-Bayesian stochastic model selection, " Mach. Learn., vol. 51, no. 1, pp. 5-21, 2003.
-
(2003)
Mach. Learn.
, vol.51
, Issue.1
, pp. 5-21
-
-
McAllester, D.A.1
-
7
-
-
84873465379
-
PAC-Bayes bounds with data dependent priors
-
Dec.
-
E. Parrado-Hernández, A. Ambroladze, J. Shawe-Taylor, and S. Sun, "PAC-Bayes bounds with data dependent priors, " J. Mach. Learn. Res., vol. 13, pp. 3507-3531, Dec. 2012.
-
(2012)
J. Mach. Learn. Res.
, vol.13
, pp. 3507-3531
-
-
Parrado-Hernández, E.1
Ambroladze, A.2
Shawe-Taylor, J.3
Sun, S.4
-
8
-
-
0032594959
-
An overview of statistical learning theory
-
Sep.
-
V. N. Vapnik, "An overview of statistical learning theory, " IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988-999, Sep. 1999.
-
(1999)
IEEE Trans. Neural Netw.
, vol.10
, Issue.5
, pp. 988-999
-
-
Vapnik, V.N.1
-
9
-
-
0032166068
-
Structural risk minimization over data-dependent hierarchies
-
Jan.
-
J. Shawe-Taylor, P. L. Bartlett, R. C. Williamson, and M. Anthony, "Structural risk minimization over data-dependent hierarchies, " IEEE Trans. Inf. Theory, vol. 44, no. 5, pp. 1926-1940, Jan. 1998.
-
(1998)
IEEE Trans. Inf. Theory
, vol.44
, Issue.5
, pp. 1926-1940
-
-
Shawe-Taylor, J.1
Bartlett, P.L.2
Williamson, R.C.3
Anthony, M.4
-
10
-
-
0038453192
-
Rademacher and Gaussian complexities: Risk bounds and structural results
-
Mar.
-
P. L. Bartlett and S. Mendelson, "Rademacher and Gaussian complexities: Risk bounds and structural results, " J. Mach. Learn. Res., vol. 3, pp. 463-482, Mar. 2003.
-
(2003)
J. Mach. Learn. Res.
, vol.3
, pp. 463-482
-
-
Bartlett, P.L.1
Mendelson, S.2
-
11
-
-
84923331837
-
Localized Rademacher complexities
-
Berlin, Germany: Springer
-
P. Bartlett, O. Bousquet, and S. Mendelson, "Localized Rademacher complexities, " in Computational Learning Theory. Berlin, Germany: Springer, 2002.
-
(2002)
Computational Learning Theory
-
-
Bartlett, P.1
Bousquet, O.2
Mendelson, S.3
-
12
-
-
84875879529
-
In-sample and out-of-sample model selection and error estimation for support vector machines
-
Jun.
-
D. Anguita, A. Ghio, L. Oneto, and S. Ridella, "In-sample and out-of-sample model selection and error estimation for support vector machines, " IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 9, pp. 1390-1406, Jun. 2012.
-
(2012)
IEEE Trans. Neural Netw. Learn. Syst.
, vol.23
, Issue.9
, pp. 1390-1406
-
-
Anguita, D.1
Ghio, A.2
Oneto, L.3
Ridella, S.4
-
13
-
-
0036643049
-
Model selection and error estimation
-
P. L. Bartlett, S. Boucheron, and G. Lugosi, "Model selection and error estimation, " Mach. Learn., vol. 48, nos. 1-3, pp. 85-113, 2002.
-
(2002)
Mach. Learn.
, vol.48
, Issue.1-3
, pp. 85-113
-
-
Bartlett, P.L.1
Boucheron, S.2
Lugosi, G.3
-
14
-
-
76749118521
-
Model selection: Beyond the Bayesian/frequentist divide
-
Jan.
-
I. Guyon, A. Saffari, G. Dror, and G. Cawley, "Model selection: Beyond the Bayesian/frequentist divide, " J. Mach. Learn. Res., vol. 11, pp. 61-87, Jan. 2010.
-
(2010)
J. Mach. Learn. Res.
, vol.11
, pp. 61-87
-
-
Guyon, I.1
Saffari, A.2
Dror, G.3
Cawley, G.4
-
15
-
-
0037551756
-
A finite sample distribution-free performance bound for local discrimination rules
-
W. H. Rogers and T. J. Wagner, "A finite sample distribution-free performance bound for local discrimination rules, " Ann. Stat., vol. 6, no. 3, pp. 506-514, 1978.
-
(1978)
Ann. Stat.
, vol.6
, Issue.3
, pp. 506-514
-
-
Rogers, W.H.1
Wagner, T.J.2
-
17
-
-
84890080043
-
A novel decision-tree method for structured continuous-label classification
-
Jan.
-
H.-W. Hu, Y.-L. Chen, and K. Tang, "A novel decision-tree method for structured continuous-label classification, " IEEE Trans. Cybern., vol. 43, no. 6, pp. 1734-1746, Jan. 2013.
-
(2013)
IEEE Trans. Cybern.
, vol.43
, Issue.6
, pp. 1734-1746
-
-
Hu, H.-W.1
Chen, Y.-L.2
Tang, K.3
-
18
-
-
33746600649
-
Reducing the dimensionality of data with neural networks
-
G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, 2006.
-
(2006)
Science
, vol.313
, Issue.5786
, pp. 504-507
-
-
Hinton, G.E.1
Salakhutdinov, R.R.2
-
19
-
-
84890110727
-
Dynamic extreme learning machine and its approximation capability
-
Feb.
-
R. Zhang, Y. Lan, G.-B. Huang, Z.-B. Xu, and Y. C. Soh, "Dynamic extreme learning machine and its approximation capability, " IEEE Trans. Cybern., vol. 43, no. 6, pp. 2054-2065, Feb. 2013.
-
(2013)
IEEE Trans. Cybern.
, vol.43
, Issue.6
, pp. 2054-2065
-
-
Zhang, R.1
Lan, Y.2
Huang, G.-B.3
Xu, Z.-B.4
Soh, Y.C.5
-
20
-
-
80052561333
-
Sets of approximating functions with finite Vapnik-Chervonenkis dimension for nearest-neighbors algorithms
-
P. Klesk and M. Korzen, "Sets of approximating functions with finite Vapnik-Chervonenkis dimension for nearest-neighbors algorithms, " Pattern Recognit. Lett., vol. 32, no. 14, pp. 1882-1893, 2011.
-
(2011)
Pattern Recognit. Lett.
, vol.32
, Issue.14
, pp. 1882-1893
-
-
Klesk, P.1
Korzen, M.2
-
21
-
-
0003421415
-
-
Philadelphia, PA, USA: SIAM
-
B. Efron, The Jackknife, the Bootstrap and Other Resampling Plans, vol. 38. Philadelphia, PA, USA: SIAM, 1982.
-
(1982)
The Jackknife, the Bootstrap and Other Resampling Plans
, vol.38
-
-
Efron, B.1
-
22
-
-
85164392958
-
A study of cross-validation and bootstrap for accuracy estimation and model selection
-
R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection, " in Proc. Int. Joint Conf. Artif. Intell., San Francisco, CA, USA, 1995, pp. 1137-1143.
-
(1995)
Proc. Int. Joint Conf. Artif. Intell., San Francisco, CA, USA
, pp. 1137-1143
-
-
Kohavi, R.1
-
23
-
-
1342330535
-
Is cross-validation valid for small-sample microarray classification?
-
U. M. Braga-Neto and E. R. Dougherty, "Is cross-validation valid for small-sample microarray classification?" Bioinformatics, vol. 20, no. 3, pp. 374-380, 2004.
-
(2004)
Bioinformatics
, vol.20
, Issue.3
, pp. 374-380
-
-
Braga-Neto, U.M.1
Dougherty, E.R.2
-
24
-
-
84954358500
-
Learning with sample dependent hypothesis spaces
-
Q. Wu and D.-X. Zhou, "Learning with sample dependent hypothesis spaces, " Comput. Math. Appl., vol. 56, no. 11, pp. 2896-2907, 2008.
-
(2008)
Comput. Math. Appl.
, vol.56
, Issue.11
, pp. 2896-2907
-
-
Wu, Q.1
Zhou, D.-X.2
-
25
-
-
0001161118
-
Machine learning with data dependent hypothesis classes
-
Jan.
-
A. Cannon, J. M. Ettinger, D. Hush, and C. Scovel, "Machine learning with data dependent hypothesis classes, " J. Mach. Learn. Res., vol. 2, pp. 335-358, Jan. 2002.
-
(2002)
J. Mach. Learn. Res.
, vol.2
, pp. 335-358
-
-
Cannon, A.1
Ettinger, J.M.2
Hush, D.3
Scovel, C.4
-
26
-
-
33746061490
-
Stability results in learning theory
-
A. Rakhlin, S. Mukherjee, and T. Poggio, "Stability results in learning theory, " Anal. Appl., vol. 3, no. 4, pp. 397-417, 2005.
-
(2005)
Anal. Appl.
, vol.3
, Issue.4
, pp. 397-417
-
-
Rakhlin, A.1
Mukherjee, S.2
Poggio, T.3
-
27
-
-
26944457342
-
Stability and generalization of bipartite ranking algorithms
-
S. Agarwal and P. Niyogi, "Stability and generalization of bipartite ranking algorithms, " in Proc. 18th Annu. Conf. Learn. Theory, Berlin, Germany, 2005, pp. 32-47.
-
(2005)
Proc. 18th Annu. Conf. Learn. Theory, Berlin, Germany
, pp. 32-47
-
-
Agarwal, S.1
Niyogi, P.2
-
28
-
-
0033566418
-
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
-
M. Kearns and D. Ron, "Algorithmic stability and sanity-check bounds for leave-one-out cross-validation, " Neural Comput., vol. 11, no. 6, pp. 1427-1453, 1999.
-
(1999)
Neural Comput.
, vol.11
, Issue.6
, pp. 1427-1453
-
-
Kearns, M.1
Ron, D.2
-
29
-
-
21844448886
-
Stability of randomized learning algorithms
-
A. Elisseeff, T. Evgeniou, and M. Pontil, "Stability of randomized learning algorithms, " J. Mach. Learn. Res., vol. 6, no. 1, p. 55, 2006.
-
(2006)
J. Mach. Learn. Res.
, vol.6
, Issue.1
, pp. 55
-
-
Elisseeff, A.1
Evgeniou, T.2
Pontil, M.3
-
30
-
-
78649409695
-
Learnability, stability and uniform convergence
-
Mar.
-
S. Shalev-Shwartz, O. Shamir, N. Srebro, and K. Sridharan, "Learnability, stability and uniform convergence, " J. Mach. Learn. Res., vol. 11, pp. 2635-2670, Mar. 2010.
-
(2010)
J. Mach. Learn. Res.
, vol.11
, pp. 2635-2670
-
-
Shalev-Shwartz, S.1
Shamir, O.2
Srebro, N.3
Sridharan, K.4
-
31
-
-
34249753618
-
Support-vector networks
-
C. Cortes and V. Vapnik, "Support-vector networks, " Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
-
(1995)
Mach. Learn.
, vol.20
, Issue.3
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
33
-
-
46349083014
-
A tail inequality for suprema of unbounded empirical processes with applications to Markov chains
-
R. Adamczak, "A tail inequality for suprema of unbounded empirical processes with applications to Markov chains, " Electron. J. Probab., vol. 13, no. 34, pp. 1000-1034, 2007.
-
(2007)
Electron. J. Probab.
, vol.13
, Issue.34
, pp. 1000-1034
-
-
Adamczak, R.1
-
34
-
-
33746063560
-
-
Dept. Comp. Sci., Univ. Chicago, Chicago, IL, USA, Tech. Rep. TR-2002-04
-
S. Kutin, "Extensions to McDiarmid's inequality when differences are bounded with high probability, " Dept. Comp. Sci., Univ. Chicago, Chicago, IL, USA, Tech. Rep. TR-2002-04, 2002.
-
(2002)
Extensions to McDiarmid's Inequality When Differences Are Bounded with High Probability
-
-
Kutin, S.1
-
35
-
-
0024646861
-
Leave-one-out procedures for non-parametric error estimates
-
Aug.
-
K. Fukunaga and D. M. Hummels, "Leave-one-out procedures for non-parametric error estimates, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 4, pp. 421-423, Aug. 1989.
-
(1989)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.11
, Issue.4
, pp. 421-423
-
-
Fukunaga, K.1
Hummels, D.M.2
-
36
-
-
2542639357
-
An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels
-
May
-
M. M. Lee, S. S. Keerthi, C. J. Ong, and D. DeCoste, "An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels, " IEEE Trans. Neural Netw., vol. 15, no. 3, pp. 750-757, May 2004.
-
(2004)
IEEE Trans. Neural Netw.
, vol.15
, Issue.3
, pp. 750-757
-
-
Lee, M.M.1
Keerthi, S.S.2
Ong, C.J.3
DeCoste, D.4
-
37
-
-
0001035413
-
On the method of bounded differences
-
C. McDiarmid, "On the method of bounded differences, " Surv. Comb., vol. 141, no. 1, pp. 148-188, 1989.
-
(1989)
Surv. Comb.
, vol.141
, Issue.1
, pp. 148-188
-
-
McDiarmid, C.1
-
38
-
-
0004019773
-
-
New York, NY, USA: Springer
-
L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, vol. 31. New York, NY, USA: Springer, 1996.
-
(1996)
A Probabilistic Theory of Pattern Recognition
, vol.31
-
-
Devroye, L.1
Györfi, L.2
Lugosi, G.3
-
39
-
-
0018444763
-
Distribution-free inequalities for the deleted and holdout error estimates
-
Jan.
-
L. Devroye and T. Wagner, "Distribution-free inequalities for the deleted and holdout error estimates, " IEEE Trans. Inf. Theory, vol. 25, no. 2, pp. 202-207, Jan. 1979.
-
(1979)
IEEE Trans. Inf. Theory
, vol.25
, Issue.2
, pp. 202-207
-
-
Devroye, L.1
Wagner, T.2
-
40
-
-
84899013173
-
Support vector regression machines
-
San Mateo, CA, USA: Morgan Kaufmann
-
H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines, " Advances in Neural Information Processing Systems. San Mateo, CA, USA: Morgan Kaufmann, 1997, pp. 155-161.
-
(1997)
Advances in Neural Information Processing Systems
, pp. 155-161
-
-
Drucker, H.1
Burges, C.J.2
Kaufman, L.3
Smola, A.4
Vapnik, V.5
-
41
-
-
12444265838
-
Consistency of support vector machines and other regularized kernel classifiers
-
Jan.
-
I. Steinwart, "Consistency of support vector machines and other regularized kernel classifiers, " IEEE Trans. Inf. Theory, vol. 51, no. 1, pp. 128-142, Jan. 2005.
-
(2005)
IEEE Trans. Inf. Theory
, vol.51
, Issue.1
, pp. 128-142
-
-
Steinwart, I.1
-
42
-
-
0000149970
-
Statistical mechanics of support vector networks
-
R. Dietrich, M. Opper, and H. Sompolinsky, "Statistical mechanics of support vector networks, " Phys. Rev. Lett., vol. 82, no. 14, p. 2975, 1999.
-
(1999)
Phys. Rev. Lett.
, vol.82
, Issue.14
, pp. 2975
-
-
Dietrich, R.1
Opper, M.2
Sompolinsky, H.3
-
43
-
-
0008238669
-
On the ability of the optimal perceptron to generalise
-
M. Opper, W. Kinzel, J. Kleinz, and R. Nehl, "On the ability of the optimal perceptron to generalise, " J. Phys. A. Math. Gen., vol. 23, no. 11, p. L581, 1990.
-
(1990)
J. Phys. A. Math. Gen.
, vol.23
, Issue.11
, pp. L581
-
-
Opper, M.1
Kinzel, W.2
Kleinz, J.3
Nehl, R.4
-
44
-
-
84939820168
-
Statistical mechanics of learning: Generalization
-
Cambridge, MA, USA: MIT Press
-
M. Opper, "Statistical mechanics of learning: Generalization, " in The Handbook of Brain Theory and Neural Networks, 2nd ed, Cambridge, MA, USA: MIT Press, 2002, pp. 922-925.
-
(2002)
The Handbook of Brain Theory and Neural Networks, 2nd Ed
, pp. 922-925
-
-
Opper, M.1
-
45
-
-
0038237368
-
Estimating dataset size requirements for classifying DNA microarray data
-
S. Mukherjee et al., "Estimating dataset size requirements for classifying DNA microarray data, " J. Comput. Biol., vol. 10, no. 2, pp. 119-142, 2003.
-
(2003)
J. Comput. Biol.
, vol.10
, Issue.2
, pp. 119-142
-
-
Mukherjee, S.1
-
46
-
-
84947403595
-
Probability inequalities for sums of bounded Random variables
-
W. Hoeffding, "Probability inequalities for sums of bounded random variables, " J. Amer. Stat. Assoc., vol. 58, no. 301, pp. 13-30, 1963.
-
(1963)
J. Amer. Stat. Assoc.
, vol.58
, Issue.301
, pp. 13-30
-
-
Hoeffding, W.1
-
47
-
-
61849142212
-
Nearly homogeneous multi-partitioning with a deterministic generator
-
M. Aupetit, "Nearly homogeneous multi-partitioning with a deterministic generator, " Neurocomputing, vol. 72, nos. 7-9, pp. 1379-1389, 2009.
-
(2009)
Neurocomputing
, vol.72
, Issue.7-9
, pp. 1379-1389
-
-
Aupetit, M.1
-
48
-
-
84947748341
-
The 'K' in K-fold cross validation
-
D. Anguita, L. Ghelardoni, A. Ghio, L. Oneto, and S. Ridella, "The 'K' in K-fold cross validation, " in Proc. Eur. Symp. Artif. Neural Netw., Bruges, Belgium, 2011, pp. 441-446.
-
(2011)
Proc. Eur. Symp. Artif. Neural Netw., Bruges, Belgium
, pp. 441-446
-
-
Anguita, D.1
Ghelardoni, L.2
Ghio, A.3
Oneto, L.4
Ridella, S.5
-
49
-
-
33745943604
-
K-fold generalization capability assessment for support vector classifiers
-
D. Anguita, S. Ridella, and F. Rivieccio, "K-fold generalization capability assessment for support vector classifiers, " in Proc. IEEE Int. Joint Conf. Neural Netw., Montreal, QC, Canada, 2005, pp. 855-858.
-
(2005)
Proc. IEEE Int. Joint Conf. Neural Netw., Montreal, QC, Canada
, pp. 855-858
-
-
Anguita, D.1
Ridella, S.2
Rivieccio, F.3
-
50
-
-
33745595398
-
SVM in Oracle database 10g: Removing the barriers to widespread adoption of support vector machines
-
B. Milenova, J. Yarmus, and M. Campos, "SVM in Oracle database 10g: Removing the barriers to widespread adoption of support vector machines, " in Proc. 31st Int. Conf. Very Large Data Bases, Trondheim, Norway, 2005, pp. 1152-1163.
-
(2005)
Proc. 31st Int. Conf. Very Large Data Bases, Trondheim, Norway
, pp. 1152-1163
-
-
Milenova, B.1
Yarmus, J.2
Campos, M.3
-
51
-
-
67649403178
-
Fast and efficient strategies for model selection of Gaussian support vector machine
-
Mar.
-
Z. Xu, M. Dai, and D. Meng, "Fast and efficient strategies for model selection of Gaussian support vector machine, " IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 5, pp. 1292-1307, Mar. 2009.
-
(2009)
IEEE Trans. Syst., Man, Cybern. B, Cybern.
, vol.39
, Issue.5
, pp. 1292-1307
-
-
Xu, Z.1
Dai, M.2
Meng, D.3
-
52
-
-
77953812676
-
Maximum likelihood model selection for 1-norm soft margin SVMS with multiple parameters
-
Apr.
-
T. Glasmachers and C. Igel, "Maximum likelihood model selection for 1-norm soft margin SVMS with multiple parameters, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 1522-1528, Apr. 2010.
-
(2010)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.32
, Issue.8
, pp. 1522-1528
-
-
Glasmachers, T.1
Igel, C.2
-
53
-
-
78651267218
-
Approximate confidence and prediction intervals for least squares support vector regression
-
Nov.
-
K. De Brabanter, J. De Brabanter, J. Suykens, and B. De Moor, "Approximate confidence and prediction intervals for least squares support vector regression, " IEEE Trans. Neural Netw., vol. 22, no. 1, pp. 110-120, Nov. 2011.
-
(2011)
IEEE Trans. Neural Netw.
, vol.22
, Issue.1
, pp. 110-120
-
-
De Brabanter, K.1
De Brabanter, J.2
Suykens, J.3
De Moor, B.4
-
54
-
-
80053646089
-
Nonlinear regularization path for quadratic loss support vector machines
-
Aug.
-
M. Karasuyama and I. Takeuchi, "Nonlinear regularization path for quadratic loss support vector machines, " IEEE Trans. Neural Netw., vol. 22, no. 10, pp. 1613-1625, Aug. 2011.
-
(2011)
IEEE Trans. Neural Netw.
, vol.22
, Issue.10
, pp. 1613-1625
-
-
Karasuyama, M.1
Takeuchi, I.2
-
55
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA, USA: MIT Press
-
J. Platt, "Fast training of support vector machines using sequential minimal optimization, " in Advances in Kernel Methods, B. Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA, USA: MIT Press, 1999, pp. 185-208.
-
(1999)
Advances in Kernel Methods
, pp. 185-208
-
-
Platt, J.1
-
56
-
-
33746869623
-
Parallel sequential minimal optimization for the training of support vector machines
-
Jul.
-
L. J. Cao et al., "Parallel sequential minimal optimization for the training of support vector machines, " IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 1039-1049, Jul. 2006.
-
(2006)
IEEE Trans. Neural Netw.
, vol.17
, Issue.4
, pp. 1039-1049
-
-
Cao, L.J.1
-
58
-
-
0037822222
-
Asymptotic behaviors of support vector machines with Gaussian kernel
-
S. Keerthi and C. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel, " Neural Comput., vol. 15, no. 7, pp. 1667-1689, 2003.
-
(2003)
Neural Comput.
, vol.15
, Issue.7
, pp. 1667-1689
-
-
Keerthi, S.1
Lin, C.2
-
59
-
-
77956649096
-
A survey of cross-validation procedures for model selection
-
S. Arlot and A. Celisse, "A survey of cross-validation procedures for model selection, " Stat. Surv., vol. 4, pp. 40-79, 2010.
-
(2010)
Stat. Surv.
, vol.4
, pp. 40-79
-
-
Arlot, S.1
Celisse, A.2
-
60
-
-
85008025524
-
Sensitivity analysis of k-fold cross validation in prediction error estimation
-
Mar.
-
J. D. Rodriguez, A. Perez, and J. A. Lozano, "Sensitivity analysis of k-fold cross validation in prediction error estimation, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 3, pp. 569-575, Mar. 2010.
-
(2010)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.32
, Issue.3
, pp. 569-575
-
-
Rodriguez, J.D.1
Perez, A.2
Lozano, J.A.3
-
61
-
-
84876917722
-
Study on the impact of partition-induced dataset shift on k-fold cross-validation
-
Jun.
-
J. G. Moreno-Torres, J. A. Sáez, and F. Herrera, "Study on the impact of partition-induced dataset shift on k-fold cross-validation, " IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 8, pp. 1304-1312, Jun. 2012.
-
(2012)
IEEE Trans. Neural Netw. Learn. Syst.
, vol.23
, Issue.8
, pp. 1304-1312
-
-
Moreno-Torres, J.G.1
Sáez, J.A.2
Herrera, F.3
-
62
-
-
79961164082
-
K-fold cross validation for error rate estimate in support vector machines
-
D. Anguita, A. Ghio, S. Ridella, and D. Sterpi, "K-fold cross validation for error rate estimate in support vector machines, " in Proc. Int. Conf. Data Mining, Las Vegas, NV, USA, 2009, pp. 291-297.
-
(2009)
Proc. Int. Conf. Data Mining, Las Vegas, NV, USA
, pp. 291-297
-
-
Anguita, D.1
Ghio, A.2
Ridella, S.3
Sterpi, D.4
-
65
-
-
0016355478
-
A new look at the statistical model identification
-
Dec.
-
H. Akaike, "A new look at the statistical model identification, " IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716-723, Dec. 1974.
-
(1974)
IEEE Trans. Autom. Control
, vol.19
, Issue.6
, pp. 716-723
-
-
Akaike, H.1
-
66
-
-
33749563073
-
Training linear SVMS in linear time
-
T. Joachims, "Training linear SVMS in linear time, " in Proc. Int. Conf. Knowl. Disc. Data Mining, Philadelphia, PA, USA, 2006, pp. 217-226.
-
(2006)
Proc. Int. Conf. Knowl. Disc. Data Mining, Philadelphia, PA, USA
, pp. 217-226
-
-
Joachims, T.1
-
67
-
-
0036583160
-
A parallel mixture of SVMS for very large scale problems
-
R. Collobert, S. Bengio, and Y. Bengio, "A parallel mixture of SVMS for very large scale problems, " Neural Comput., vol. 14, no. 5, pp. 1105-1114, 2002.
-
(2002)
Neural Comput.
, vol.14
, Issue.5
, pp. 1105-1114
-
-
Collobert, R.1
Bengio, S.2
Bengio, Y.3
-
68
-
-
33947417485
-
Very large SVM training using core vector machines
-
I. W. Tsang, J. T. Kwok, and P.-M. Cheung, "Very large SVM training using core vector machines, " in Proc. Int. Workshop Artif. Intell. Stat., 2005, pp. 349-356.
-
(2005)
Proc. Int. Workshop Artif. Intell. Stat.
, pp. 349-356
-
-
Tsang, I.W.1
Kwok, J.T.2
Cheung, P.-M.3
-
69
-
-
80052955921
-
A review of optimization methodologies in support vector machines
-
J. Shawe-Taylor and S. Sun, "A review of optimization methodologies in support vector machines, " Neurocomputing, vol. 74, no. 17, pp. 3609-3618, 2011.
-
(2011)
Neurocomputing
, vol.74
, Issue.17
, pp. 3609-3618
-
-
Shawe-Taylor, J.1
Sun, S.2
-
70
-
-
0000545946
-
Improvements to Platt's SMO algorithm for SVM classifier design
-
S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy, "Improvements to Platt's SMO algorithm for SVM classifier design, " Neural Comput., vol. 13, no. 3, pp. 637-649, 2001.
-
(2001)
Neural Comput.
, vol.13
, Issue.3
, pp. 637-649
-
-
Keerthi, S.S.1
Shevade, S.K.2
Bhattacharyya, C.3
Murthy, K.R.K.4
-
71
-
-
29144499905
-
Working set selection using second order information for training support vector machines
-
Dec.
-
R.-E. Fan, P.-H. Chen, and C.-J. Lin, "Working set selection using second order information for training support vector machines, " J. Mach. Learn. Res., vol. 6, pp. 1889-1918, Dec. 2005.
-
(2005)
J. Mach. Learn. Res.
, vol.6
, pp. 1889-1918
-
-
Fan, R.-E.1
Chen, P.-H.2
Lin, C.-J.3
-
73
-
-
80053620586
-
Multiple incremental decremental learning of support vector machines
-
M. Karasuyama and I. Takeuchi, "Multiple incremental decremental learning of support vector machines, " in Proc. Adv. Neural Inf. Process. Syst., 2009, pp. 1048-1059.
-
(2009)
Proc. Adv. Neural Inf. Process. Syst.
, pp. 1048-1059
-
-
Karasuyama, M.1
Takeuchi, I.2
-
74
-
-
0033400675
-
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
-
J. A. Blackard and D. J. Dean, "Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables, " Comput. Electron. Agr., vol. 24, no. 3, pp. 131-151, 1999.
-
(1999)
Comput. Electron. Agr.
, vol.24
, Issue.3
, pp. 131-151
-
-
Blackard, J.A.1
Dean, D.J.2
-
75
-
-
33846581659
-
An experimental study on pedestrian classification
-
Nov.
-
S. Munder and D. M. Gavrila, "An experimental study on pedestrian classification, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 11, pp. 1863-1868, Nov. 2006.
-
(2006)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.28
, Issue.11
, pp. 1863-1868
-
-
Munder, S.1
Gavrila, D.M.2
-
76
-
-
85089251671
-
Comparison of classifier methods: A case study in handwritten digit recognition
-
Oct.
-
L. Bottou et al., "Comparison of classifier methods: A case study in handwritten digit recognition, " in Proc. Int. Conf. Pattern Recognit. Conf. B Comput. Vis. Image Process., Jerusalem, Israel, Oct. 1994, pp. 77-82.
-
(1994)
Proc. Int. Conf. Pattern Recognit. Conf. B Comput. Vis. Image Process., Jerusalem, Israel
, pp. 77-82
-
-
Bottou, L.1
-
77
-
-
84939820170
-
-
Google (Books/OCR), Tech. Rep [Online]
-
Y. Bulatov, (2011). "Notmnist dataset, " Google (Books/OCR), Tech. Rep. [Online]. Available: http://yaroslavvb.blogspot.it/2011/09/notmnist-dataset.html
-
(2011)
Notmnist Dataset
-
-
Bulatov, Y.1
-
78
-
-
0036505670
-
A comparison of methods for multiclass support vector machines
-
Mar.
-
C.-W. Hsu and C.-J. Lin, "A comparison of methods for multiclass support vector machines, " IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
-
(2002)
IEEE Trans. Neural Netw.
, vol.13
, Issue.2
, pp. 415-425
-
-
Hsu, C.-W.1
Lin, C.-J.2
-
79
-
-
4944228528
-
-
Dept. Comput. Sci., Nat. Taiwan Univ., Tech. Rep. [Online]
-
C.-W. Hsu, C.-C. Chang, and C.-J. Lin, "A practical guide to support vector classification, " Dept. Comput. Sci., Nat. Taiwan Univ., Tech. Rep., 2003 [Online]. Available http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf
-
(2003)
A Practical Guide to Support Vector Classification
-
-
Hsu, C.-W.1
Chang, C.-C.2
Lin, C.-J.3
-
80
-
-
0030522124
-
A new look at independence
-
M. Talagrand, "A new look at independence, " Ann. Probab., vol. 24, no. 1, pp. 1-34, 1996.
-
(1996)
Ann. Probab.
, vol.24
, Issue.1
, pp. 1-34
-
-
Talagrand, M.1
-
81
-
-
0034344120
-
A sharp concentration inequality with applications, Random
-
May
-
S. Boucheron, G. Lugosi, and P. Massart, "A sharp concentration inequality with applications, " Random Struct. Algorithms, vol. 16, no. 3, pp. 277-292, May 2000.
-
(2000)
Struct. Algorithms
, vol.16
, Issue.3
, pp. 277-292
-
-
Boucheron, S.1
Lugosi, G.2
Massart, P.3
-
82
-
-
0037561860
-
A Bennett concentration inequality and its application to suprema of empirical processes
-
O. Bousquet, "A Bennett concentration inequality and its application to suprema of empirical processes, " CR Math., vol. 334, no. 6, pp. 495-500, 2002.
-
(2002)
CR Math.
, vol.334
, Issue.6
, pp. 495-500
-
-
Bousquet, O.1
-
83
-
-
85162053390
-
Smoothness, low-noise, and fast rates
-
N. Srebro, K. Sridharan, and A. Tewari, "Smoothness, low-noise, and fast rates, " in Proc. Neural Inf. Process. Syst., 2010, pp. 2199-2207.
-
(2010)
Proc. Neural Inf. Process. Syst.
, pp. 2199-2207
-
-
Srebro, N.1
Sridharan, K.2
Tewari, A.3
-
84
-
-
0033234630
-
Smooth discrimination analysis
-
E. Mammen and A. B. Tsybakov, "Smooth discrimination analysis, " Ann. Stat., vol. 27, no. 6, pp. 1808-1829, 1999.
-
(1999)
Ann. Stat.
, vol.27
, Issue.6
, pp. 1808-1829
-
-
Mammen, E.1
Tsybakov, A.B.2
-
85
-
-
68649088331
-
Fast learning rates in statistical inference through aggregation
-
J.-Y. Audibert, "Fast learning rates in statistical inference through aggregation, " Ann. Stat., vol. 37, no. 4, pp. 1591-1646, 2009.
-
(2009)
Ann. Stat.
, vol.37
, Issue.4
, pp. 1591-1646
-
-
Audibert, J.-Y.1
|