-
2
-
-
3142725535
-
Semi-supervised learning on riemannian manifolds
-
M. Belkin and P. Niyogi. Semi-Supervised Learning on Riemannian Manifolds. Machine Learning, 56(1-3):209-239, 2004.
-
(2004)
Machine Learning
, vol.56
, Issue.1-3
, pp. 209-239
-
-
Belkin, M.1
Niyogi, P.2
-
3
-
-
0033435207
-
A first-generation X-incativation profile of the human X chromosome
-
I. Carrel, A. Cottle, K. Coglin, and H. Willard. A first-generation X-incativation profile of the human X chromosome. Proc. Natl. Acad. Sci. USA, 96:14440-14444, 1999.
-
(1999)
Proc. Natl. Acad. Sci. USA
, vol.96
, pp. 14440-14444
-
-
Carrel, I.1
Cottle, A.2
Coglin, K.3
Willard, H.4
-
4
-
-
0036161011
-
Choosing multiple parameters for support vector machines
-
O. Chapelle, V. N. Vapnik, O. Bousquet, and S. Mukherjee. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1-3):131-159, 2002.
-
(2002)
Machine Learning
, vol.46
, Issue.1-3
, pp. 131-159
-
-
Chapelle, O.1
Vapnik, V.N.2
Bousquet, O.3
Mukherjee, S.4
-
5
-
-
0032131292
-
Atomic decomposition by basis pursuit
-
S. S. Chen, D. L. Donoho, and M. A. Saunders. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1):33-61, 1999.
-
(1999)
SIAM Journal on Scientific Computing
, vol.20
, Issue.1
, pp. 33-61
-
-
Chen, S.S.1
Donoho, D.L.2
Saunders, M.A.3
-
6
-
-
34249753618
-
Support-vector networks
-
C. Cortes and V. N. Vapnik. Support-Vector Networks. Machine Learning, 20(3):273-297, 1995.
-
(1995)
Machine Learning
, vol.20
, Issue.3
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.N.2
-
7
-
-
0036071370
-
On the mathematical foundations of learning
-
F. Cucker and S. Smale. On the mathematical foundations of learning. Bull. Amer. Math. Soc., 39: 1-49, 2001.
-
(2001)
Bull. Amer. Math. Soc.
, vol.39
, pp. 1-49
-
-
Cucker, F.1
Smale, S.2
-
10
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
-
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, and E. S. Lander. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531-537, 1999.
-
(1999)
Science
, vol.286
, pp. 531-537
-
-
Golub, T.R.1
Slonim, D.K.2
Tamayo, P.3
Huard, C.4
Gaasenbeek, M.5
Mesirov, J.P.6
Coller, H.7
Loh, M.L.8
Downing, J.R.9
Caligiuri, M.A.10
Bloomfield, C.D.11
Lander, E.S.12
-
11
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46(1-3):389-422, 2002.
-
(2002)
Machine Learning
, vol.46
, Issue.1-3
, pp. 389-422
-
-
Guyon, I.1
Weston, J.2
Barnhill, S.3
Vapnik, V.4
-
12
-
-
2142775432
-
Multicategory support vector machines: Theory and applications to the classification of microarray data and satellite radiance data
-
Y. Lee, Y. Lin, and G. Wahba. Multicategory support vector machines: theory and applications to the classification of microarray data and satellite radiance data. Journal of the American Statistical Society, 99:67-81, 2004.
-
(2004)
Journal of the American Statistical Society
, vol.99
, pp. 67-81
-
-
Lee, Y.1
Lin, Y.2
Wahba, G.3
-
15
-
-
14544299611
-
On learning vector-valued functions
-
C. A. Micchelli and M. Pontil. On learning vector-valued functions. Neural Computation, 17: 177-204, 2005.
-
(2005)
Neural Computation
, vol.17
, pp. 177-204
-
-
Micchelli, C.A.1
Pontil, M.2
-
16
-
-
0001638327
-
Optimum bounds for the distributions of martingales in Banach spaces
-
I. Pinelis. Optimum bounds for the distributions of martingales in Banach spaces. Ann. Probab., 22:1679-1706, 1994.
-
(1994)
Ann. Probab.
, vol.22
, pp. 1679-1706
-
-
Pinelis, I.1
-
17
-
-
33646361736
-
Correction: "Optimum bounds for the distributions of martingales in Banach spaces"
-
I. Pinelis. Correction: "Optimum bounds for the distributions of martingales in Banach spaces". Ann. Probab., 27:2119, 1999.
-
(1999)
Ann. Probab.
, vol.27
, pp. 2119
-
-
Pinelis, I.1
-
18
-
-
0025056697
-
Regularization algorithms for learning that are equivalent to multilayer networks
-
T. Poggio and F. Girosi. Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247:978-982, 1990.
-
(1990)
Science
, vol.247
, pp. 978-982
-
-
Poggio, T.1
Girosi, F.2
-
19
-
-
0003408420
-
-
MIT Press, Cambridge, MA, USA
-
B. Schoelkopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA, 2001.
-
(2001)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and beyond
-
-
Schoelkopf, B.1
Smola, A.2
-
20
-
-
0033726346
-
Class prediction and discovery using gene expression data
-
D. K. Slonim, P. Tamayo, J. P. Mesirov, T. R. Golub, and E. S. Lander. Class prediction and discovery using gene expression data. In Proc. of the 4th Annual International Conference on Computational Molecular Biology (RECOMB), pages 263-272, 2000.
-
(2000)
Proc. of the 4th Annual International Conference on Computational Molecular Biology (RECOMB)
, pp. 263-272
-
-
Slonim, D.K.1
Tamayo, P.2
Mesirov, J.P.3
Golub, T.R.4
Lander, E.S.5
-
21
-
-
33646361487
-
Learning theory estimates via integral operators and their approximations
-
S. Smale and D. X. Zhou. Learning theory estimates via integral operators and their approximations. Constr. Approx., 24, 2006a.
-
(2006)
Constr. Approx.
, vol.24
-
-
Smale, S.1
Zhou, D.X.2
-
22
-
-
27844555491
-
Shannon sampling II. Connections to learning theory
-
S. Smale and D. X. Zhou. Shannon sampling II. Connections to learning theory. Appl. Comput. Harmonic Anal., 19:285-302, 2006b.
-
(2006)
Appl. Comput. Harmonic Anal.
, vol.19
, pp. 285-302
-
-
Smale, S.1
Zhou, D.X.2
-
23
-
-
3042850649
-
Shannon sampling and function reconstruction from point values
-
S. Smale and D. X. Zhou. Shannon sampling and function reconstruction from point values. Bull. Amer. Math. Soc., 41:279-305, 2004.
-
(2004)
Bull. Amer. Math. Soc.
, vol.41
, pp. 279-305
-
-
Smale, S.1
Zhou, D.X.2
-
24
-
-
0037749769
-
Estimating the approximation error in learning theory
-
S. Smale and D. X. Zhou. Estimating the approximation error in learning theory. Anal. Appl., 1: 17-41, 2003.
-
(2003)
Anal. Appl.
, vol.1
, pp. 17-41
-
-
Smale, S.1
Zhou, D.X.2
-
25
-
-
27344435774
-
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
-
A. Subramanian, P. Tamayo, VK. Mootha, S. Mukherjee, BL. Ebert, MA. Gillette, A. Paulovich, SL. Pomeroy, TR. Golub, ES. Lander, and JP. Mesirov. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 2005.
-
(2005)
Proc Natl Acad Sci U S A
-
-
Subramanian, A.1
Tamayo, P.2
Mootha, V.K.3
Mukherjee, S.4
Ebert, B.L.5
Gillette, Ma.6
Paulovich, A.7
Pomeroy, S.L.8
Golub, T.R.9
Lander, E.S.10
Mesirov, J.P.11
-
26
-
-
11244258643
-
An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis
-
A. Sweet-Cordero, S. Mukherjee, A. Subramanian, H. You, J. J. Roix, C. Ladd-Acosta, J. P. Mesirov, T. R. Golub, and T. Jacks. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nature Genetics, 37:48-55, 2005.
-
(2005)
Nature Genetics
, vol.37
, pp. 48-55
-
-
Sweet-Cordero, A.1
Mukherjee, S.2
Subramanian, A.3
You, H.4
Roix, J.J.5
Ladd-Acosta, C.6
Mesirov, J.P.7
Golub, T.R.8
Jacks, T.9
-
27
-
-
0001287271
-
Regression shrinkage and selection via the lasso
-
R. Tibshirani. Regression shrinkage and selection via the lasso. J Royal Stat Soc B, 58(1):267-288, 1996.
-
(1996)
J Royal Stat Soc B
, vol.58
, Issue.1
, pp. 267-288
-
-
Tibshirani, R.1
-
29
-
-
24944432318
-
Model selection for regularized least-squares algorithm in learning
-
E. De Vito, A. Caponnetto, and L. Rosasco. Model selection for regularized least-squares algorithm in learning. Foundat. Comput. Math., 5:59-85, 2005.
-
(2005)
Foundat. Comput. Math.
, vol.5
, pp. 59-85
-
-
De Vito, E.1
Caponnetto, A.2
Rosasco, L.3
-
30
-
-
0000681041
-
Some new mathematical methods for variational objective analysis using splines and cross-validation
-
G. Wahba and J. Wendelberger. Some new mathematical methods for variational objective analysis using splines and cross-validation. Monthly Weather Rev., 108:1122-1145, 1980.
-
(1980)
Monthly Weather Rev.
, vol.108
, pp. 1122-1145
-
-
Wahba, G.1
Wendelberger, J.2
-
31
-
-
0242295767
-
Bayesian factor regression models in the "large p, small n" paradigm
-
J. M. Bernardo et al., editor, Oxford
-
M. West. Bayesian factor regression models in the "large p, small n" paradigm. In J. M. Bernardo et al., editor, Bayesian Statistics 7, pages 723-732. Oxford, 2003.
-
(2003)
Bayesian Statistics
, vol.7
, pp. 723-732
-
-
West, M.1
-
32
-
-
17444402055
-
Support vector machine classifiers: Linear programming versus quadratic programming
-
Q. Wu and D. X. Zhou. Support vector machine classifiers: linear programming versus quadratic programming. Neural Computation, 17:1160-1187, 2005.
-
(2005)
Neural Computation
, vol.17
, pp. 1160-1187
-
-
Wu, Q.1
Zhou, D.X.2
-
33
-
-
0042879446
-
Leave-one-out bounds for kernel methods
-
T. Zhang. Leave-one-out bounds for kernel methods. Neural Computation, 15(6): 1397-1437, 2003.
-
(2003)
Neural Computation
, vol.15
, Issue.6
, pp. 1397-1437
-
-
Zhang, T.1
-
34
-
-
0038105204
-
Capacity of reproducing kernel spaces in learning theory
-
D. X. Zhou. Capacity of reproducing kernel spaces in learning theory. IEEE Trans. Inform. Theory, 49:1743-1752, 2003.
-
(2003)
IEEE Trans. Inform. Theory
, vol.49
, pp. 1743-1752
-
-
Zhou, D.X.1
|