-
1
-
-
77950441864
-
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
-
Bartók, A.P., Payne, M.C., Kondor, R., Csányi, G.: Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104, 136403 (2010)
-
(2010)
Phys. Rev. Lett
, vol.104
-
-
Bartók, A.P.1
Payne, M.C.2
Kondor, R.3
Csányi, G.4
-
2
-
-
0034296402
-
Generalized discriminant analysis using a kernel approach
-
Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Comput. 12(10), 2385-2404 (2000)
-
(2000)
Neural Comput
, vol.12
, Issue.10
, pp. 2385-2404
-
-
Baudat, G.1
Anouar, F.2
-
3
-
-
0042378381
-
Laplacian eigenmaps for dimensionality reduction and data representation
-
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373-1396 (2003)
-
(2003)
Neural Comput
, vol.15
, Issue.6
, pp. 1373-1396
-
-
Belkin, M.1
Niyogi, P.2
-
4
-
-
0026966646
-
A training algorithm for optimal margin classifiers
-
Haussler, D. (ed.)
-
Boser, B., Guyon, I., Vapnik, V.: A training algorithm for optimal margin classifiers. In: Haussler, D. (ed.) Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, pp. 144-152 (1992)
-
(1992)
Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh
, pp. 144-152
-
-
Boser, B.1
Guyon, I.2
Vapnik, V.3
-
5
-
-
0004990788
-
Mathematical programming for data mining: Formulations and challenges
-
Bradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. J. Comput. 11(3), 217-238 (1999)
-
(1999)
J. Comput
, vol.11
, Issue.3
, pp. 217-238
-
-
Bradley, P.1
Fayyad, U.2
Mangasarian, O.3
-
6
-
-
50949127418
-
On relevant dimensions in kernel feature spaces
-
Braun, M., Buhmann, J., Müller, K.R.: On relevant dimensions in kernel feature spaces. J. Mach. Learn. Res. 9, 1875-1908 (2008)
-
(2008)
J. Mach. Learn. Res
, vol.9
, pp. 1875-1908
-
-
Braun, M.1
Buhmann, J.2
Müller, K.R.3
-
7
-
-
27144489164
-
A tutorial on support vector machines for pattern recognition
-
Burges, C.: A tutorial on support vector machines for pattern recognition. Knowl. Discov. Data Min. 2(2), 121-167 (1998)
-
(1998)
Knowl. Discov. Data Min
, vol.2
, Issue.2
, pp. 121-167
-
-
Burges, C.1
-
8
-
-
84860123425
-
Perspective on density functional theory
-
Burke, K.: Perspective on density functional theory. J. Chem. Phys. 136(15), 150,901 (2012)
-
(2012)
J. Chem. Phys
, vol.136
, Issue.15
-
-
Burke, K.1
-
10
-
-
34249753618
-
Support vector networks
-
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273-297 (1995)
-
(1995)
Mach. Learn
, vol.20
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
12
-
-
0037948870
-
Hessian eigenmaps: Locally linear embedding techniques for highdimensional data
-
Donoho, D.L., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for highdimensional data. Proc. Natl. Acad. Sci. 100(10), 5591-5596 (2003)
-
(2003)
Proc. Natl. Acad. Sci
, vol.100
, Issue.10
, pp. 5591-5596
-
-
Donoho, D.L.1
Grimes, C.2
-
14
-
-
84876258641
-
Learning hierarchical features for scene labeling
-
(in press)
-
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. (2013, in press)
-
(2013)
IEEE Trans. Pattern Anal. Mach. Intell
-
-
Farabet, C.1
Couprie, C.2
Najman, L.3
LeCun, Y.4
-
15
-
-
84958962514
-
Kernel canonical correlation analysis and least squares support vector machines
-
Gestel, T.V., Suykens, J.A.K., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Kernel canonical correlation analysis and least squares support vector machines. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001), Vienna, pp. 381-386 (2001)
-
(2001)
Proceedings of the International Conference on Artificial Neural Networks (ICANN 2001), Vienna
, pp. 381-386
-
-
Gestel, T.V.1
Suykens, J.A.K.2
Brabanter, J.D.3
Moor, B.D.4
Vandewalle, J.5
-
16
-
-
0041376445
-
Kernel-based nonlinear blind source separation
-
Harmeling, S., Ziehe, A., Kawanabe, M., Müller, K.R.: Kernel-based nonlinear blind source separation. Neural Comput. 15, 1089-1124 (2003)
-
(2003)
Neural Comput
, vol.15
, pp. 1089-1124
-
-
Harmeling, S.1
Ziehe, A.2
Kawanabe, M.3
Müller, K.R.4
-
17
-
-
0003684449
-
-
2nd edn, Springer, New York
-
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2009)
-
(2009)
The Elements of Statistical Learning. Data Mining, Inference, and Prediction
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.3
-
18
-
-
10644250257
-
Inhomogeneous electron gas
-
Hohenberg, P., Kohn, W.: Inhomogeneous electron gas. Phys. Rev. B 136(3B), 864-871 (1964)
-
(1964)
Phys. Rev. B
, vol.136
, Issue.3 B
, pp. 864-871
-
-
Hohenberg, P.1
Kohn, W.2
-
19
-
-
0002714543
-
Making large-scale SVM learning practical
-
Schölkopf, B., Burges, C., Smola, A. (eds.), MIT, Cambridge
-
Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Learning, pp. 169-184. MIT, Cambridge (1999)
-
(1999)
Advances in Kernel Methods-Support Vector Learning
, pp. 169-184
-
-
Joachims, T.1
-
20
-
-
0042113153
-
Self-consistent equations including exchange and correlation effects
-
Kohn, W., Sham, L.J.: Self-consistent equations including exchange and correlation effects. Phys. Rev. A 140(4A), 1133-1138 (1965)
-
(1965)
Phys. Rev. A
, vol.140
, Issue.4 A
, pp. 1133-1138
-
-
Kohn, W.1
Sham, L.J.2
-
21
-
-
33745777639
-
Incremental support vector learning: Analysis, implementation and applications
-
Laskov, P., Gehl, C., Krüger, S., Müller, K.R.: Incremental support vector learning: analysis, implementation and applications. J. Mach. Learn. Res. 7, 1909-1936 (2006)
-
(2006)
J. Mach. Learn. Res
, vol.7
, pp. 1909-1936
-
-
Laskov, P.1
Gehl, C.2
Krüger, S.3
Müller, K.R.4
-
22
-
-
0033337021
-
Fisher discriminant analysis with kernels
-
Hu, Y.H., Larsen, J., Wilson, E., Douglas, S. (eds.), IEEE, New York
-
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Müller, K.R.: Fisher discriminant analysis with kernels. In: Hu, Y.H., Larsen, J., Wilson, E., Douglas, S. (eds.) Neural Networks for Signal Processing IX, pp. 41-48. IEEE, New York (1999)
-
(1999)
Neural Networks for Signal Processing IX
, pp. 41-48
-
-
Mika, S.1
Rätsch, G.2
Weston, J.3
Schölkopf, B.4
Müller, K.R.5
-
23
-
-
84898970836
-
Kernel PCA and de-noising in feature spaces
-
Kearns, M., Solla, S., Cohn, D. (eds.), MIT, Cambridge
-
Mika, S., Schölkopf, B., Smola, A., Müller, K.R., Scholz, M., Rätsch, G.: Kernel PCA and de-noising in feature spaces. In: Kearns, M., Solla, S., Cohn, D. (eds.) Advances in Neural Information Processing Systems, vol. 11, pp. 536-542. MIT, Cambridge (1999)
-
(1999)
Advances in Neural Information Processing Systems
, vol.11
, pp. 536-542
-
-
Mika, S.1
Schölkopf, B.2
Smola, A.3
Müller, K.R.4
Scholz, M.5
Rätsch, G.6
-
24
-
-
0038633559
-
Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces
-
Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.R.: Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces. IEEE Trans. Patterns Anal. Mach. Intell. 25(5), 623-627 (2003)
-
(2003)
IEEE Trans. Patterns Anal. Mach. Intell
, vol.25
, Issue.5
, pp. 623-627
-
-
Mika, S.1
Rätsch, G.2
Weston, J.3
Schölkopf, B.4
Smola, A.5
Müller, K.R.6
-
25
-
-
85032751251
-
Analyzing local structure in kernel-based learning: Explanation, complexity and reliability assessment
-
Montavon, G., Braun, M., Krüger, T., Müller, K.R.: Analyzing local structure in kernel-based learning: explanation, complexity and reliability assessment. IEEE Signal Process. Mag. 30(4), 62-74 (2013)
-
(2013)
IEEE Signal Process. Mag
, vol.30
, Issue.4
, pp. 62-74
-
-
Montavon, G.1
Braun, M.2
Krüger, T.3
Müller, K.R.4
-
26
-
-
80555140085
-
A kernel analysis of deep networks
-
Montavon, G., Braun, M., Müller, K.R.: A kernel analysis of deep networks. J. Mach. Learn. Res. 12, 2579-2597 (2011)
-
(2011)
J. Mach. Learn. Res
, vol.12
, pp. 2579-2597
-
-
Montavon, G.1
Braun, M.2
Müller, K.R.3
-
27
-
-
84872533173
-
Big learning and deep neural networks
-
Montavon, G., Orr, G.B., Müller, K.R. (eds.), Springer, Berlin/Heidelberg
-
Montavon, G., Müller, K.R.: Big learning and deep neural networks. In: Montavon, G., Orr, G.B., Müller, K.R. (eds.) Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, pp. 419-420. Springer, Berlin/Heidelberg (2012)
-
(2012)
Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science
, vol.7700
, pp. 419-420
-
-
Montavon, G.1
Müller, K.R.2
-
28
-
-
0004135065
-
Neural Networks: Tricks of the Trade
-
Springer, New York
-
Montavon, G., Orr, G., Müller, K.R. (eds.): Neural Networks: Tricks of the Trade, vol. 7700. In: LNCS. Springer, New York (2012)
-
(2012)
LNCS
, vol.7700
-
-
Montavon, G.1
Orr, G.2
Müller, K.R.3
-
29
-
-
0035272287
-
An introduction to kernel-based learning algorithms
-
Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181-201 (2001)
-
(2001)
IEEE Trans. Neural Netw
, vol.12
, Issue.2
, pp. 181-201
-
-
Müller, K.R.1
Mika, S.2
Rätsch, G.3
Tsuda, K.4
Schölkopf, B.5
-
30
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
Schölkopf, B., Burges, C., Smola, A. (eds.), MIT, Cambridge
-
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185-208. MIT, Cambridge (1999)
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 185-208
-
-
Platt, J.1
-
31
-
-
84862891798
-
Optimizing transition states via kernel-based machine learning
-
Pozun, Z.D., Hansen, K., Sheppard, D., Rupp, M., Müller, K.R., Henkelman, G.: Optimizing transition states via kernel-based machine learning. J. Chem. Phys. 136(17), 174101 (2012)
-
(2012)
J. Chem. Phys
, vol.136
, Issue.17
-
-
Pozun, Z.D.1
Hansen, K.2
Sheppard, D.3
Rupp, M.4
Müller, K.R.5
Henkelman, G.6
-
32
-
-
0034704222
-
Nonlinear dimensionality reduction by locally linear embedding
-
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323-2326 (2000)
-
(2000)
Science
, vol.290
, Issue.5500
, pp. 2323-2326
-
-
Roweis, S.T.1
Saul, L.K.2
-
33
-
-
84856512353
-
Fast and accurate modeling of molecular atomization energies with machine learning
-
Rupp, M., Tkatchenko, A., Müller, K.R., von Lilienfeld, O.A.: Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108(5), 058301 (2012)
-
(2012)
Phys. Rev. Lett
, vol.108
, Issue.5
-
-
Rupp, M.1
Tkatchenko, A.2
Müller, K.R.3
von Lilienfeld, O.A.4
-
34
-
-
0347243182
-
Nonlinear component analysis as a kernel eigenvalue problem
-
Schölkopf, B., Smola, A., Müller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural comput. 10(5), 1299-1319 (1998)
-
(1998)
Neural comput
, vol.10
, Issue.5
, pp. 1299-1319
-
-
Schölkopf, B.1
Smola, A.2
Müller, K.3
-
35
-
-
0032594954
-
Input space versus feature space in kernel-based methods
-
Scholkopf, B., Mika, S., Burges, C., Knirsch, P., Muller, K.R., Ratsch, G., Smola, A.: Input space versus feature space in kernel-based methods. IEEE Trans. Neural Netw. 10, 1000-1017 (1999)
-
(1999)
IEEE Trans. Neural Netw
, vol.10
, pp. 1000-1017
-
-
Scholkopf, B.1
Mika, S.2
Burges, C.3
Knirsch, P.4
Muller, K.R.5
Ratsch, G.6
Smola, A.7
-
36
-
-
0000487102
-
Estimating the support of a high-dimensional distribution
-
Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443-1471 (2001)
-
(2001)
Neural Comput
, vol.13
, Issue.7
, pp. 1443-1471
-
-
Schölkopf, B.1
Platt, J.2
Shawe-Taylor, J.3
Smola, A.4
Williamson, R.5
-
37
-
-
0037845137
-
Regularized principal manifolds
-
Smola, A., Mika, S., Schölkopf, B., Williamson, R.: Regularized principal manifolds. J. Mach. Learn. Res. 1, 179-209 (2001)
-
(2001)
J. Mach. Learn. Res
, vol.1
, pp. 179-209
-
-
Smola, A.1
Mika, S.2
Schölkopf, B.3
Williamson, R.4
-
38
-
-
84862560607
-
Finding density functionals with machine learning
-
Snyder, J.C., Rupp, M., Hansen, K., Müller, K.R., Burke, K.: Finding density functionals with machine learning. Phys. Rev. Lett. 108, 253002 (2012)
-
(2012)
Phys. Rev. Lett
, vol.108
-
-
Snyder, J.C.1
Rupp, M.2
Hansen, K.3
Müller, K.R.4
Burke, K.5
-
39
-
-
84903362304
-
Orbital-free bond breaking via machine learning
-
Submitted to
-
Snyder, J.C., Rupp, M., Hansen, K., Blooston, L., Müller, K.R., Burke, K.: Orbital-free bond breaking via machine learning. Submitted to J. Chem. Phys. (2013)
-
(2013)
J. Chem. Phys
-
-
Snyder, J.C.1
Rupp, M.2
Hansen, K.3
Blooston, L.4
Müller, K.R.5
Burke, K.6
-
41
-
-
84899032239
-
The relevance vector machine
-
Solla, S., Leen, T., Müller, K.R. (eds.), MIT, Cambridge
-
Tipping, M.: The relevance vector machine. In: Solla, S., Leen, T., Müller, K.R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 652-658. MIT, Cambridge (2000)
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 652-658
-
-
Tipping, M.1
-
42
-
-
23044525572
-
Scaling kernel-based systems to large data sets
-
Tresp, V.: Scaling kernel-based systems to large data sets. Data Min. Knowl. Discov. 5, 197-211 (2001)
-
(2001)
Data Min. Knowl. Discov
, vol.5
, pp. 197-211
-
-
Tresp, V.1
-
44
-
-
56549124687
-
Improve local tangent space alignment using various dimensional local coordinates
-
Wang, J.: Improve local tangent space alignment using various dimensional local coordinates. Neurocomputing 71(16), 3575-3581 (2008)
-
(2008)
Neurocomputing
, vol.71
, Issue.16
, pp. 3575-3581
-
-
Wang, J.1
-
45
-
-
84867920399
-
Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
-
Zhang, Z.Y., Zha, H.Y.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. J. Shanghai University (English Edition) 8(4), 406-424 (2004)
-
(2004)
J. Shanghai University (English Edition)
, vol.8
, Issue.4
, pp. 406-424
-
-
Zhang, Z.Y.1
Zha, H.Y.2
|