-
1
-
-
84893703288
-
Human activity recognition using multi-features and multiple kernel learning
-
S. Althloothi, M. Mahoor, Zhang X., and R. Voyles Human activity recognition using multi-features and multiple kernel learning Pattern Recognit. 47 2014 1800 1812
-
(2014)
Pattern Recognit.
, vol.47
, pp. 1800-1812
-
-
Althloothi, S.1
Mahoor, M.2
Zhang, X.3
Voyles, R.4
-
2
-
-
84893704508
-
Imbalanced data classification using second-order cone programming support vector machines
-
S. Maldonado, and J. López Imbalanced data classification using second-order cone programming support vector machines Pattern Recognit. 47 5 2014 2070 2079
-
(2014)
Pattern Recognit.
, vol.47
, Issue.5
, pp. 2070-2079
-
-
Maldonado, S.1
López, J.2
-
3
-
-
84903197687
-
Multiple kernel learning for visual object recognition: A review
-
S. Bucak, Jin R., and A. Jain Multiple kernel learning for visual object recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 36 7 2014 1354 1369
-
(2014)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.36
, Issue.7
, pp. 1354-1369
-
-
Bucak, S.1
Jin, R.2
Jain, A.3
-
4
-
-
84921627833
-
Two-stage multiple kernel learning for supervised dimensionality reduction
-
A. Nazarpour, and P. Adibi Two-stage multiple kernel learning for supervised dimensionality reduction Pattern Recognit. 48 5 2014 1854 1862
-
(2014)
Pattern Recognit.
, vol.48
, Issue.5
, pp. 1854-1862
-
-
Nazarpour, A.1
Adibi, P.2
-
5
-
-
8844278523
-
Learning the kernel matrix with semi-definite programming
-
G. Lanckriet, N. Cristianini, P. Bartlett, L. Ghaoui, and M. Jordan Learning the kernel matrix with semi-definite programming J. Mach. Learn. Res. 5 2004 27 72
-
(2004)
J. Mach. Learn. Res.
, vol.5
, pp. 27-72
-
-
Lanckriet, G.1
Cristianini, N.2
Bartlett, P.3
Ghaoui, L.4
Jordan, M.5
-
6
-
-
14344252374
-
Multiple kernel learning, conic duality, and the SMO algorithm
-
ACM
-
F.R. Bach, G.R. Lanckriet, and M.I. Jordan Multiple kernel learning, conic duality, and the SMO algorithm Proceedings of International Conference on Machine Learning 2004 ACM 6 13
-
(2004)
Proceedings of International Conference on Machine Learning
, pp. 6-13
-
-
Bach, F.R.1
Lanckriet, G.R.2
Jordan, M.I.3
-
8
-
-
79953713204
-
Design of a multiple kernel learning algorithm for LS-SVM by convex programming
-
Jian L., Xia Z., Liang X., and Gao C. Design of a multiple kernel learning algorithm for LS-SVM by convex programming Neural Networks 24 5 2011 476 483
-
(2011)
Neural Networks
, vol.24
, Issue.5
, pp. 476-483
-
-
Jian, L.1
Xia, Z.2
Liang, X.3
Gao, C.4
-
9
-
-
34547971778
-
More efficiency in multiple kernel learning
-
A. Rakotomamonjy, F. Bach, S. Canu, and Y. Grandvalet More efficiency in multiple kernel learning Int. Conf. Mach. Learn. 7 2006 2007 775 782
-
(2007)
Int. Conf. Mach. Learn.
, vol.7
, Issue.2006
, pp. 775-782
-
-
Rakotomamonjy, A.1
Bach, F.2
Canu, S.3
Grandvalet, Y.4
-
10
-
-
77949784467
-
Sparse multiple kernel learning for signal processing applications
-
N. Subrahmanya, and Shi Y. Sparse multiple kernel learning for signal processing applications IEEE Trans. Pattern Anal. Mach. Intell. 32 2010 788 798
-
(2010)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.32
, pp. 788-798
-
-
Subrahmanya, N.1
Shi, Y.2
-
12
-
-
84870238770
-
Localized algorithms for multiple kernel learning
-
M. Gönen, and E. AlpaydIn Localized algorithms for multiple kernel learning Pattern Recognit. 46 3 2013 795 807
-
(2013)
Pattern Recognit.
, vol.46
, Issue.3
, pp. 795-807
-
-
Gönen, M.1
Alpaydin, E.2
-
13
-
-
84938202759
-
EasyMKL: A scalable multiple kernel learning algorithm
-
F. Aiolli, and M. Donini EasyMKL: a scalable multiple kernel learning algorithm Neurocomputing 169 2015 215 224
-
(2015)
Neurocomputing
, vol.169
, pp. 215-224
-
-
Aiolli, F.1
Donini, M.2
-
14
-
-
84929963662
-
A pre-selecting base kernel method in multiple kernel learning
-
Wu P., Duan F., and Guo P. A pre-selecting base kernel method in multiple kernel learning Neurocomputing 165 2015 46 53
-
(2015)
Neurocomputing
, vol.165
, pp. 46-53
-
-
Wu, P.1
Duan, F.2
Guo, P.3
-
15
-
-
0037276932
-
Face recognition using kernel direct discriminant analysis algorithms
-
Lu J., K. Plataniotis, and A. Venetsanopoulos Face recognition using kernel direct discriminant analysis algorithms IEEE Trans. Neural Netw. 14 1 2003 117 126
-
(2003)
IEEE Trans. Neural Netw.
, vol.14
, Issue.1
, pp. 117-126
-
-
Lu, J.1
Plataniotis, K.2
Venetsanopoulos, A.3
-
16
-
-
21844447839
-
Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems
-
Ye J. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems J. Mach. Learn. Res. 6 2005 483 502
-
(2005)
J. Mach. Learn. Res.
, vol.6
, pp. 483-502
-
-
Ye, J.1
-
17
-
-
14744274588
-
Using uncorrelated discriminant analysis for tissue classification with gene expression data
-
Ye J., Li T., Xiong T., and R. Janardan Using uncorrelated discriminant analysis for tissue classification with gene expression data IEEE Trans. Comput. Biol. Bioinform. 1 4 2004 181 190
-
(2004)
IEEE Trans. Comput. Biol. Bioinform.
, vol.1
, Issue.4
, pp. 181-190
-
-
Ye, J.1
Li, T.2
Xiong, T.3
Janardan, R.4
-
18
-
-
15344339935
-
Optimizing the kernel in the empirical feature space
-
Xiong H., M. Swamy, and M.O. Ahmad Optimizing the kernel in the empirical feature space IEEE Trans. Neural Netw. 16 2 2005 460 474
-
(2005)
IEEE Trans. Neural Netw.
, vol.16
, Issue.2
, pp. 460-474
-
-
Xiong, H.1
Swamy, M.2
Ahmad, M.O.3
-
22
-
-
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. 25 2006 161 193
-
(2006)
Adv. Comput. Math.
, vol.25
, pp. 161-193
-
-
Mukherjee, S.1
Niyogi, P.2
Poggio, T.3
Rifkin, R.4
-
23
-
-
0042378381
-
Laplacian eigenmaps for dimensionality reduction and data representation
-
M. Belkin, and P. Niyogi Laplacian eigenmaps for dimensionality reduction and data representation Neural Comput. 15 6 2003 1373 1396
-
(2003)
Neural Comput.
, vol.15
, Issue.6
, pp. 1373-1396
-
-
Belkin, M.1
Niyogi, P.2
-
24
-
-
15044358511
-
Face recognition using Laplacian faces
-
He X., Yan S., Hu Y., Niyogi P., and Zhang H. Face recognition using Laplacian faces IEEE Trans. Pattern Anal. Mach. Intell. 27 3 2005 328 339
-
(2005)
IEEE Trans. Pattern Anal. Mach. Intell.
, vol.27
, Issue.3
, pp. 328-339
-
-
He, X.1
Yan, S.2
Hu, Y.3
Niyogi, P.4
Zhang, H.5
-
25
-
-
33644903839
-
Face recognition using discriminant locality preserving projections
-
Yu W., Teng X., and Liu C. Face recognition using discriminant locality preserving projections Image Vis. Comput. 24 3 2006 239 248
-
(2006)
Image Vis. Comput.
, vol.24
, Issue.3
, pp. 239-248
-
-
Yu, W.1
Teng, X.2
Liu, C.3
-
26
-
-
77955321700
-
Locality preserving discriminant projections for face and palmprint recognition
-
Gui J., Jia W., Zhu L., Wang S., and Huang D. Locality preserving discriminant projections for face and palmprint recognition Neurocomputing 73 2010 2696 2707
-
(2010)
Neurocomputing
, vol.73
, pp. 2696-2707
-
-
Gui, J.1
Jia, W.2
Zhu, L.3
Wang, S.4
Huang, D.5
-
27
-
-
84870284479
-
Generalized locality preserving Maxi-Min margin machine
-
Zhang Z., Choi K., Lou X., and Wang S. Generalized locality preserving Maxi-Min margin machine Neural Netw. 36 2012 18 24
-
(2012)
Neural Netw.
, vol.36
, pp. 18-24
-
-
Zhang, Z.1
Choi, K.2
Lou, X.3
Wang, S.4
-
29
-
-
84908504635
-
Globality-locality preserving projections for biometric data dimensionality reduction
-
Huang S., A. Elgammal, Huangfu L., Yang D., and Zhang X. Globality-locality preserving projections for biometric data dimensionality reduction IEEE Conference on Computer Vision and Pattern Recognition 2014 15 20
-
(2014)
IEEE Conference on Computer Vision and Pattern Recognition
, pp. 15-20
-
-
Huang, S.1
Elgammal, A.2
Huangfu, L.3
Yang, D.4
Zhang, X.5
-
30
-
-
77952586109
-
Regularized locality preserving projections and its extensions for face recognition
-
Lu J., and Tan Y. Regularized locality preserving projections and its extensions for face recognition IEEE Trans. Syst. Man. Cybern. Part B 40 2010 958 963
-
(2010)
IEEE Trans. Syst. Man. Cybern. Part B
, vol.40
, pp. 958-963
-
-
Lu, J.1
Tan, Y.2
-
31
-
-
84867881228
-
Enhanced and parameterless locality preserving projections for face recognition
-
F. Dornaika, and A. Assoum enhanced and parameterless locality preserving projections for face recognition Neurocomputing 99 2013 448 457
-
(2013)
Neurocomputing
, vol.99
, pp. 448-457
-
-
Dornaika, F.1
Assoum, A.2
-
32
-
-
84893654461
-
Locality and similarity preserving embedding for feature selection
-
Fang X., Xu Y., Li X., Fan Z., Liu H., and Chen Y. Locality and similarity preserving embedding for feature selection Neurocomputing 128 2014 304 315
-
(2014)
Neurocomputing
, vol.128
, pp. 304-315
-
-
Fang, X.1
Xu, Y.2
Li, X.3
Fan, Z.4
Liu, H.5
Chen, Y.6
-
33
-
-
84879556490
-
Training lp-norm multiple kernel learning in the primal
-
Liang Z., Xia S., Zhou Y., and Zhang L. Training lp-norm multiple kernel learning in the primal Neural Netw. 46 2013 172 182
-
(2013)
Neural Netw.
, vol.46
, pp. 172-182
-
-
Liang, Z.1
Xia, S.2
Zhou, Y.3
Zhang, L.4
-
34
-
-
0042842443
-
Ho-Kashyap classifier with generalization control
-
J. Łkeski Ho-Kashyap classifier with generalization control Pattern Recognit. Lett. 24 14 2003 2281 2290
-
(2003)
Pattern Recognit. Lett.
, vol.24
, Issue.14
, pp. 2281-2290
-
-
Łkeski, J.1
-
37
-
-
84903184768
-
Multiple kernel learning for sparse representation-based classification
-
S. Ashish, V. Patel, and C. Rama Multiple kernel learning for sparse representation-based classification IEEE Trans. Image Process. 23 7 2014 3013 3024
-
(2014)
IEEE Trans. Image Process.
, vol.23
, Issue.7
, pp. 3013-3024
-
-
Ashish, S.1
Patel, V.2
Rama, C.3
-
38
-
-
33846294051
-
Kernel Ho-Kashyap classifier with generalization control
-
J. Leski Kernel Ho-Kashyap classifier with generalization control Int. J. Appl. Math. Comput. Sci. 14 1 2004 53 62
-
(2004)
Int. J. Appl. Math. Comput. Sci.
, vol.14
, Issue.1
, pp. 53-62
-
-
Leski, J.1
-
39
-
-
84886567160
-
-
University of California, Irvine, School of Information and Computer Sciences
-
K. Bache, and M. Lichman UCI Machine Learning Repository 2013 University of California, Irvine, School of Information and Computer Sciences http://archive.ics.uci.edu/ml
-
(2013)
UCI Machine Learning Repository
-
-
Bache, K.1
Lichman, M.2
-
40
-
-
84929503174
-
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
-
Wong T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation Pattern Recognit. 48 9 2015 2839 2846
-
(2015)
Pattern Recognit.
, vol.48
, Issue.9
, pp. 2839-2846
-
-
Wong, T.1
-
41
-
-
29644438050
-
Statistical comparisons of classifiers over multiple data sets
-
J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
-
(2006)
J. Mach. Learn. Res.
, vol.7
, pp. 1-30
-
-
Demšar, J.1
-
43
-
-
0035419334
-
Support vector machines for face recognition
-
Guo G., Li S.Z., and Chan K. Support vector machines for face recognition Image Vis. Comput. 19 9-10 2001 631 638
-
(2001)
Image Vis. Comput.
, vol.19
, Issue.9-10
, pp. 631-638
-
-
Guo, G.1
Li, S.Z.2
Chan, K.3
-
45
-
-
0035397715
-
Rademacher penalties and structural risk minimization
-
V. Koltchinskii Rademacher penalties and structural risk minimization IEEE Trans. Inf. Theory 47 5 2001 1902 1914
-
(2001)
IEEE Trans. Inf. Theory
, vol.47
, Issue.5
, pp. 1902-1914
-
-
Koltchinskii, V.1
-
46
-
-
0001024505
-
On the uniform convergence of relative frequencies of events to their probabilities
-
V. Vapnik, and A. Chervonenkis On the uniform convergence of relative frequencies of events to their probabilities Theory Probab. Appl. 16 2 1971 264 280
-
(1971)
Theory Probab. Appl.
, vol.16
, Issue.2
, pp. 264-280
-
-
Vapnik, V.1
Chervonenkis, A.2
-
47
-
-
0036104545
-
Empirical margin distributions and bounding the generalization error of combined classifiers
-
V. Koltchinskii, and D. Panchenko Empirical margin distributions and bounding the generalization error of combined classifiers Ann. Stat. 30 1 2002 1 50
-
(2002)
Ann. Stat.
, vol.30
, Issue.1
, pp. 1-50
-
-
Koltchinskii, V.1
Panchenko, D.2
|