-
1
-
-
85141266799
-
Support vector machines for multiple-instance learning
-
S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multiple-instance learning. In NIPS, pages 561-568. 2002.
-
(2002)
NIPS
, pp. 561-568
-
-
Andrews, S.1
Tsochantaridis, I.2
Hofmann, T.3
-
2
-
-
84945797434
-
Dynamic Node Creation in Backpropagation Networks
-
T. Ash. Dynamic Node Creation in Backpropagation Networks. Connection Science, 1(4):365-375, 1989.
-
(1989)
Connection Science
, vol.1
, Issue.4
, pp. 365-375
-
-
Ash, T.1
-
3
-
-
70450188146
-
Visual Tracking with Online Multiple Instance Learning
-
B. Babenko, M.-H. Yang, and S. Belongie. Visual Tracking with Online Multiple Instance Learning. In CVPR, 2009.
-
(2009)
CVPR
-
-
Babenko, B.1
Yang, M.-H.2
Belongie, S.3
-
4
-
-
0013309537
-
On-line learning and stochastic approximations
-
D. Saad, editor, Cambridge University Press
-
L. Bottou. On-line learning and stochastic approximations. In D. Saad, editor, On-Line Learning in Neural Networks, pages 9-42. Cambridge University Press, 1999.
-
(1999)
On-Line Learning in Neural Networks
, pp. 9-42
-
-
Bottou, L.1
-
5
-
-
85042678229
-
FilterBoost: Regression and Classification on Large Datasets
-
J. Bradley and R. Schapire. FilterBoost: Regression and Classification on Large Datasets. In NIPS, 2007.
-
(2007)
NIPS
-
-
Bradley, J.1
Schapire, R.2
-
6
-
-
84947441494
-
Blobworld: A system for region-based image indexing and retrieval
-
C. Carson, M. Thomas, S. Belongie, J. Hellerstein, and J. Malik. Blobworld: A system for region-based image indexing and retrieval. Third International Conference on Visual Information Systems, pages 509-516, 1999.
-
(1999)
Third International Conference on Visual Information Systems
, pp. 509-516
-
-
Carson, C.1
Thomas, M.2
Belongie, S.3
Hellerstein, J.4
Malik, J.5
-
7
-
-
80052866161
-
Incremental and Decremental Support Vector Machine Learning
-
G. Cauwenberghs and T. Poggio. Incremental and Decremental Support Vector Machine Learning. In NIPS, pages 409-415. 2001.
-
(2001)
NIPS
, pp. 409-415
-
-
Cauwenberghs, G.1
Poggio, T.2
-
8
-
-
34547972773
-
Boosting for transfer learning
-
ACM New York, NY, USA
-
W. Dai, Q. Yang, G. Xue, and Y. Yu. Boosting for transfer learning. In ICML, pages 193-200. ACM New York, NY, USA, 2007.
-
(2007)
ICML
, pp. 193-200
-
-
Dai, W.1
Yang, Q.2
Xue, G.3
Yu, Y.4
-
9
-
-
0030649484
-
Solving the multiple instance problem with axis-parallel rectangles
-
PII S0004370296000343
-
T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solving the multiple-instance problem with axis parallel rectangles. Artificial Intelligence, 89(1-2):31-71, 1997. (Pubitemid 127412230)
-
(1997)
Artificial Intelligence
, vol.89
, Issue.1-2
, pp. 31-71
-
-
Dietterich, T.G.1
Lathrop, R.H.2
Lozano-Perez, T.3
-
10
-
-
4644367942
-
An efficient boosting algorithm for combining preferences
-
Y. Freund, R. Iyer, R. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. The Journal of Machine Learning Research, 4:933-969, 2003.
-
(2003)
The Journal of Machine Learning Research
, vol.4
, pp. 933-969
-
-
Freund, Y.1
Iyer, R.2
Schapire, R.3
Singer, Y.4
-
11
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application to boosting
-
Y. Freund and R. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
-
(1997)
Journal of Computer and System Sciences
, vol.55
, Issue.1
, pp. 119-139
-
-
Freund, Y.1
Schapire, R.2
-
12
-
-
0035470889
-
Greedy function approximation: A gradient boosting machine
-
J. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5):1189-1232, 2001.
-
(2001)
Annals of Statistics
, vol.29
, Issue.5
, pp. 1189-1232
-
-
Friedman, J.1
-
14
-
-
33845570001
-
On-line boosting and vision
-
H. Grabner and H. Bischof. On-line boosting and vision. In CVPR, pages 260-267. 2006.
-
(2006)
CVPR
, pp. 260-267
-
-
Grabner, H.1
Bischof, H.2
-
15
-
-
56749152262
-
Semi-Supervised On-line Boosting for Robust Tracking?
-
H. Grabner, C. Leistner, and H. Bischof. Semi-Supervised On-line Boosting for Robust Tracking? In ECCV, pages 234-247, 2008.
-
(2008)
ECCV
, pp. 234-247
-
-
Grabner, H.1
Leistner, C.2
Bischof, H.3
-
16
-
-
28244499387
-
Semi-Supervised MarginBoost
-
Y. Grandvalet and C. Ambroise. Semi-Supervised MarginBoost. In NIPS, pages 553-560. 2002.
-
(2002)
NIPS
, pp. 553-560
-
-
Grandvalet, Y.1
Ambroise, C.2
-
17
-
-
51849117118
-
-
University of Massachusetts, Amherst, Technical Report 07-49
-
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49, 2007.
-
(2007)
Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments
-
-
Huang, G.B.1
Ramesh, M.2
Berg, T.3
Learned-Miller, E.4
-
20
-
-
85081805923
-
Incremental learning for visual tracking
-
J. Lim, D. Ross, R. Lin, and M.-H. Yang. Incremental learning for visual tracking. NIPS, 17:793-800, 2005.
-
(2005)
NIPS
, vol.17
, pp. 793-800
-
-
Lim, J.1
Ross, D.2
Lin, R.3
Yang, M.-H.4
-
21
-
-
50649114155
-
Gradient feature selection for online boosting
-
X. Liu and T. Yu. Gradient feature selection for online boosting. In ICCV. 2007.
-
(2007)
ICCV
-
-
Liu, X.1
Yu, T.2
-
22
-
-
84898978212
-
Boosting algorithms as gradient descent in function space
-
L. Mason, J. Baxter, P. Bartlett, and M. Frean. Boosting algorithms as gradient descent in function space. In NIPS, volume 12, pages 512-518, 2000.
-
(2000)
NIPS
, vol.12
, pp. 512-518
-
-
Mason, L.1
Baxter, J.2
Bartlett, P.3
Frean, M.4
-
23
-
-
1942483164
-
-
Ph.D. Thesis, University of California Berkeley
-
N. C. Oza. Online Ensemble Learning. Ph.D. Thesis, University of California, Berkeley, 2001.
-
(2001)
Online Ensemble Learning
-
-
Oza, N.C.1
-
24
-
-
31844448950
-
Supervised versus multiple instance learning: An empirical comparison
-
S. Ray and M. Craven. Supervised versus multiple instance learning: an empirical comparison. ICML, pages 697-704, 2005.
-
(2005)
ICML
, pp. 697-704
-
-
Ray, S.1
Craven, M.2
-
25
-
-
11144273669
-
The perceptron
-
F. Rosenblatt. The perceptron. Psychical Review, 65(6):386-408, 1958.
-
(1958)
Psychical Review
, vol.65
, Issue.6
, pp. 386-408
-
-
Rosenblatt, F.1
-
26
-
-
0022471098
-
Learning representations by back-propagating errors
-
D. Rumelhart, G. Hinton, and R. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533-536, 1986.
-
(1986)
Nature
, vol.323
, Issue.6088
, pp. 533-536
-
-
Rumelhart, D.1
Hinton, G.2
Williams, R.3
-
27
-
-
0025448521
-
The strength of weak learnability
-
R. Schapire. The strength of weak learnability. Machine Learning, 5(2):197-227, 1990.
-
(1990)
Machine Learning
, vol.5
, Issue.2
, pp. 197-227
-
-
Schapire, R.1
-
28
-
-
0031246271
-
Decision Tree Induction Based on Efficient Tree Restructuring
-
P. Utgoff, N. Berkman, and J. Clouse. Decision Tree Induction Based on Efficient Tree Restructuring. Mach. Learn., 29(1):5-44, 1997.
-
(1997)
Mach. Learn.
, vol.29
, Issue.1
, pp. 5-44
-
-
Utgoff, P.1
Berkman, N.2
Clouse, J.3
-
29
-
-
84864049528
-
Multiple instance boosting for object detection
-
P. Viola, J. Platt, and C. Zhang. Multiple instance boosting for object detection. In NIPS, pages 1417-1424. 2006.
-
(2006)
NIPS
, pp. 1417-1424
-
-
Viola, P.1
Platt, J.2
Zhang, C.3
-
31
-
-
84898985725
-
A gradient-based boosting algorithm for regression problems
-
R. Zemel and T. Pitassi. A gradient-based boosting algorithm for regression problems. In NIPS, pages 696-702. 2001.
-
(2001)
NIPS
, pp. 696-702
-
-
Zemel, R.1
Pitassi, T.2
-
32
-
-
0037355948
-
Sequential greedy approximation for certain convex optimization problems
-
T. Zhang. Sequential greedy approximation for certain convex optimization problems. IEEE Trans. on Inf. Theory, 49(3):682-691, 2003.
-
(2003)
IEEE Trans. on Inf. Theory
, vol.49
, Issue.3
, pp. 682-691
-
-
Zhang, T.1
|