-
1
-
-
33745561205
-
An introduction to variable and feature selection
-
I. Guyon and A. Elisseeff. An introduction to variable and feature selection. JMLR, 3:1157-1182, 2003.
-
(2003)
JMLR
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
2
-
-
70450161376
-
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers
-
P. V. Gehler and S. Nowozin. Let the kernel figure it out; principled learning of pre-processing for kernel classifiers. CVPR, 2009.
-
(2009)
CVPR
-
-
Gehler, P.V.1
Nowozin, S.2
-
3
-
-
79551576499
-
Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population
-
C. Hinrichs, V. Singh, G. Xu, and S.C. Johnson. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. Neuroimage, 55(2):574-589, 2011.
-
(2011)
Neuroimage
, vol.55
, Issue.2
, pp. 574-589
-
-
Hinrichs, C.1
Singh, V.2
Xu, G.3
Johnson, S.C.4
-
4
-
-
79952073234
-
Multimodal classification of Alzheimer's disease and mild cognitive impairment
-
D. Zhang, Y. Wang, L. Zhou, H. Yuan, and D. Shen. Multimodal Classification of Alzheimer's Disease and Mild Cognitive Impairment. NeuroImage, 55(3):856-867, 2011.
-
(2011)
NeuroImage
, vol.55
, Issue.3
, pp. 856-867
-
-
Zhang, D.1
Wang, Y.2
Zhou, L.3
Yuan, H.4
Shen, D.5
-
5
-
-
8844278523
-
Learning the kernel matrix with semidefinite programming
-
G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan. Learning the kernel matrix with semidefinite programming. JMLR, 5:27-72, 2004.
-
(2004)
JMLR
, vol.5
, pp. 27-72
-
-
Lanckriet, G.R.G.1
Cristianini, N.2
Bartlett, P.3
El Ghaoui, L.4
Jordan, M.5
-
6
-
-
33745776113
-
Large scale multiple kernel learning
-
S. Sonnenburg, G. Rätsch, C. Schäfer, and B. Schölkopf. Large scale multiple kernel learning. JMLR, 7:1531-1565, 2006.
-
(2006)
JMLR
, vol.7
, pp. 1531-1565
-
-
Sonnenburg, S.1
Rätsch, G.2
Schäfer, C.3
Schölkopf, B.4
-
7
-
-
57249084590
-
SimpleMKL
-
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet. SimpleMKL. JMLR, 9:2491-2521, 2008.
-
(2008)
JMLR
, vol.9
, pp. 2491-2521
-
-
Rakotomamonjy, A.1
Bach, F.2
Canu, S.3
Grandvalet, Y.4
-
8
-
-
85067032737
-
On feature combination for multiclass object classification
-
P. V. Gehler and S. Nowozin. On feature combination for multiclass object classification. In ICCV, 2009.
-
(2009)
ICCV
-
-
Gehler, P.V.1
Nowozin, S.2
-
9
-
-
77952494909
-
Group-sensitive multiple kernel learning for object categorization
-
J. Yang, Y. Li, Y. Tian, L. Duan, and W. Gao. Group-sensitive multiple kernel learning for object categorization. In ICCV, 2009.
-
(2009)
ICCV
-
-
Yang, J.1
Li, Y.2
Tian, Y.3
Duan, L.4
Gao, W.5
-
10
-
-
37849014569
-
Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
-
P. Vemuri, J.L. Gunter, M. L. Senjem, J. L. Whitwell, K. Kantarci, D. S. Knopman, et al. Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage, 39(3):1186-1197, 2008.
-
(2008)
Neuroimage
, vol.39
, Issue.3
, pp. 1186-1197
-
-
Vemuri, P.1
Gunter, J.L.2
Senjem, M.L.3
Whitwell, J.L.4
Kantarci, K.5
Knopman, D.S.6
-
11
-
-
79952039980
-
Composite kernel learning
-
M. Szafranski, Y. Grandvalet, and A. Rakotomamonjy. Composite kernel learning. Machine learning, 79(1):73-103, 2010.
-
(2010)
Machine Learning
, vol.79
, Issue.1
, pp. 73-103
-
-
Szafranski, M.1
Grandvalet, Y.2
Rakotomamonjy, A.3
-
12
-
-
14344252374
-
Multiple kernel learning, conic duality, and the SMO algorithm
-
F. R. Bach, G. Lanckriet, and M. I. Jordan. Multiple kernel learning, conic duality, and the SMO algorithm. In ICML, 2004.
-
(2004)
ICML
-
-
Bach, F.R.1
Lanckriet, G.2
Jordan, M.I.3
-
13
-
-
77955993905
-
Online-batch strongly convex multi Kernel learning
-
F. Orabona, L. Jie, and B. Caputo. Online-Batch Strongly Convex Multi Kernel Learning. In CVPR, 2010.
-
(2010)
CVPR
-
-
Orabona, F.1
Jie, L.2
Caputo, B.3
-
15
-
-
21844468979
-
Learning the kernel with hyperkernels
-
C.S. Ong, A. Smola, and B. Williamson. Learning the kernel with hyperkernels. JMLR, 6:1045-1071, 2005.
-
(2005)
JMLR
, vol.6
, pp. 1045-1071
-
-
Ong, C.S.1
Smola, A.2
Williamson, B.3
-
16
-
-
77956000518
-
Learning Kernels for variants of normalized cuts: Convex relaxations and applications
-
L. Mukherjee, V. Singh, J. Peng, and C. Hinrichs. Learning Kernels for variants of Normalized Cuts: Convex Relaxations and Applications. CVPR, 2010.
-
(2010)
CVPR
-
-
Mukherjee, L.1
Singh, V.2
Peng, J.3
Hinrichs, C.4
-
18
-
-
85027388762
-
Exploring large feature spaces with hierarchical multiple kernel learning
-
F. R. Bach. Exploring large feature spaces with hierarchical multiple kernel learning. In NIPS, 2008.
-
(2008)
NIPS
-
-
Bach, F.R.1
-
22
-
-
77949873915
-
Kernel entropy component analysis
-
R. Jenssen. Kernel entropy component analysis. IEEE Trans. PAMI, pages 847-860, 2009.
-
(2009)
IEEE Trans. PAMI
, pp. 847-860
-
-
Jenssen, R.1
-
23
-
-
0012993529
-
Orthogonal series density estimation and the kernel eigenvalue problem
-
M. Girolami. Orthogonal series density estimation and the kernel eigenvalue problem. Neural Computation, 14(3):669-688, 2002.
-
(2002)
Neural Computation
, vol.14
, Issue.3
, pp. 669-688
-
-
Girolami, M.1
-
24
-
-
0036737108
-
Generalized information potential criterion for adaptive system training
-
D. Erdogmus and J.C. Principe. Generalized information potential criterion for adaptive system training. IEEE Trans. Neural Networks, 13(5):1035-1044, 2002.
-
(2002)
IEEE Trans. Neural Networks
, vol.13
, Issue.5
, pp. 1035-1044
-
-
Erdogmus, D.1
Principe, J.C.2
-
25
-
-
71149091247
-
Multiple indefinite kernel learning with mixed norm regularization
-
M. Kowalski, M. Szafranski, and L. Ralaivola. Multiple indefinite kernel learning with mixed norm regularization. In ICML, 2009.
-
(2009)
ICML
-
-
Kowalski, M.1
Szafranski, M.2
Ralaivola, L.3
-
26
-
-
84862273336
-
Improved natural language learning via variance-regularization support vector machines
-
S. Bergsma, D. Lin, and D. Schuurmans. Improved Natural Language Learning via Variance-Regularization Support Vector Machines. In CoNLL, 2010.
-
(2010)
CoNLL
-
-
Bergsma, S.1
Lin, D.2
Schuurmans, D.3
-
27
-
-
85161983244
-
Spatial and anatomical regularization of SVM for brain image analysis
-
R. Cuingnet, M. Chupin, H. Benali, and O. Colliot. Spatial and anatomical regularization of SVM for brain image analysis. In NIPS, 2010.
-
(2010)
NIPS
-
-
Cuingnet, R.1
Chupin, M.2
Benali, H.3
Colliot, O.4
-
28
-
-
77949509297
-
Maximum relative margin and data-dependent regularization
-
P. Shivaswamy and T. Jebara. Maximum relative margin and data-dependent regularization. JMLR, 11:747-788, 2010.
-
(2010)
JMLR
, vol.11
, pp. 747-788
-
-
Shivaswamy, P.1
Jebara, T.2
-
29
-
-
85161982879
-
Learning kernels with radiuses of minimum enclosing balls
-
K. Gai, G. Chen, and C. Zhang. Learning kernels with radiuses of minimum enclosing balls. In NIPS, 2010.
-
(2010)
NIPS
-
-
Gai, K.1
Chen, G.2
Zhang, C.3
-
30
-
-
33144484244
-
Ways toward an early diagnosis in Alzheimers disease: The Alzheimer's disease neuroimaging initiative
-
S. G. Mueller, M. W. Weiner, et al. Ways toward an early diagnosis in Alzheimers disease: The Alzheimer's Disease Neuroimaging Initiative. J. of the Alzheimer's Association, 1(1):55-66, 2005.
-
(2005)
J. of the Alzheimer's Association
, vol.1
, Issue.1
, pp. 55-66
-
-
Mueller, S.G.1
Weiner, M.W.2
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