-
1
-
-
49949144765
-
The relaxation method of finding a common point of convex sets and its application to the solution of problems in convex programming
-
BREGMAN, L. M. (1967). The relaxation method of finding a common point of convex sets and its application to the solution of problems in convex programming. U.S.S.R. Computational Mathematics and Mathematical Physics 7 200-217.
-
(1967)
U.S.S.R. Computational Mathematics and Mathematical Physics
, vol.7
, pp. 200-217
-
-
Bregman, L.M.1
-
2
-
-
0346786584
-
Arcing classifiers
-
BREIMAN, L. (1998). Arcing classifiers (with discussion). Ann. Statist. 26 801-849.
-
(1998)
Ann. Statist.
, vol.26
, pp. 801-849
-
-
Breiman, L.1
-
3
-
-
0000275022
-
Prediction games and arcing algorithms
-
BREIMAN, L. (1999). Prediction games and arcing algorithms. Neural Computation 11 1493-1517.
-
(1999)
Neural Computation
, vol.11
, pp. 1493-1517
-
-
Breiman, L.1
-
4
-
-
0013228784
-
Some infinity theory for predictor ensembles
-
Dept. Statistics, Univ. California, Berkeley
-
BREIMAN, L. (2000). Some infinity theory for predictor ensembles. Technical Report 577, Dept. Statistics, Univ. California, Berkeley.
-
(2000)
Technical Report
, vol.577
-
-
Breiman, L.1
-
6
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application to boosting
-
FREUND, Y. and SCHAPIRE, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System Sci. 55 119-139.
-
(1997)
J. Comput. System Sci.
, vol.55
, pp. 119-139
-
-
Freund, Y.1
Schapire, R.E.2
-
7
-
-
0034164230
-
Additive logistic regression: A statistical view of boosting
-
FRIEDMAN, J., HASTIE, T. and TIBSHIRANI, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28 337-407.
-
(2000)
Ann. Statist.
, vol.28
, pp. 337-407
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
8
-
-
0027262895
-
Multilayer feedforward networks with a non-polynomial activation function can approximate any function
-
LESHNO, M., LIN, YA. V., PINKUS, A. and SCHOCKEN, S. (1993). Multilayer feedforward networks with a non-polynomial activation function can approximate any function. Neural Networks 6 861-867.
-
(1993)
Neural Networks
, vol.6
, pp. 861-867
-
-
Leshno, M.1
Lin, Ya.V.2
Pinkus, A.3
Schocken, S.4
-
9
-
-
9444269961
-
On the Bayes-risk consistency of regularized boosting methods
-
LUGOSI, G. and VAYATIS, N. (2004). On the Bayes-risk consistency of regularized boosting methods. Ann. Statist. 32 30-55.
-
(2004)
Ann. Statist.
, vol.32
, pp. 30-55
-
-
Lugosi, G.1
Vayatis, N.2
-
10
-
-
84937440094
-
The consistency of greedy algorithms for classification
-
Springer, New York
-
MANNOR, S., MEIR, R. and ZHANG, T. (2002). The consistency of greedy algorithms for classification. In Proc. 15th Annual Conference on Computational Learning Theory. Lecture Notes in Comput. Sci. 2375 319-333. Springer, New York.
-
(2002)
Proc. 15th Annual Conference on Computational Learning Theory. Lecture Notes in Comput. Sci.
, vol.2375
, pp. 319-333
-
-
Mannor, S.1
Meir, R.2
Zhang, T.3
-
13
-
-
0032280519
-
Boosting the margin: A new explanation for the effectiveness of voting methods
-
SCHAPIRE, R. E., FREUND, Y., BARTLETT, P. and LEE, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Statist. 26 1651-1686.
-
(1998)
Ann. Statist.
, vol.26
, pp. 1651-1686
-
-
Schapire, R.E.1
Freund, Y.2
Bartlett, P.3
Lee, W.S.4
-
14
-
-
0033281701
-
Improved boosting algorithms using confidence-rated predictions
-
SCHAPIRE, R. E. and SINGER, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 297-336.
-
(1999)
Machine Learning
, vol.37
, pp. 297-336
-
-
Schapire, R.E.1
Singer, Y.2
-
15
-
-
0036749277
-
Support vector machines are universally consistent
-
STEINWART, I. (2002). Support vector machines are universally consistent. J. Complexity 18 768-791.
-
(2002)
J. Complexity
, vol.18
, pp. 768-791
-
-
Steinwart, I.1
-
18
-
-
84880171586
-
A leave-one-out cross validation bound for kernel methods with applications in learning
-
Springer, New York
-
ZHANG, T. (2001). A leave-one-out cross validation bound for kernel methods with applications in learning. In Proc. 14th Annual Conference on Computational Learning Theory 427-443. Springer, New York.
-
(2001)
Proc. 14th Annual Conference on Computational Learning Theory
, pp. 427-443
-
-
Zhang, T.1
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