-
1
-
-
0004493166
-
On the Approximability of Minimizing non zero Variables or Unsatisfied Relations in Linear Systems
-
E. Amaldi and V. Kann. On the Approximability of Minimizing non zero Variables or Unsatisfied Relations in Linear Systems. Theoretical Computer Science, 209: 237-260, 1998.
-
(1998)
Theoretical Computer Science
, vol.209
, pp. 237-260
-
-
Amaldi, E.1
Kann, V.2
-
2
-
-
1542365112
-
Dimensionality Reduction via Sparse Support Vector Machines
-
J. Bi, K. Bennett, M. Embrechts, C. Breneman, and M. Song. Dimensionality Reduction via Sparse Support Vector Machines. Journal of Machine Learning Research, 3:1229-1243, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1229-1243
-
-
Bi, J.1
Bennett, K.2
Embrechts, M.3
Breneman, C.4
Song, M.5
-
4
-
-
0002709342
-
Feature Selection via Concave Minimization and Support Vector Machines
-
Morgan Kaufmann, San Francisco, CA
-
P. S. Bradley and O. L. Mangasarian. Feature Selection via Concave Minimization and Support Vector Machines. In Proc. 15th International Conf. on Alachine Learning, pages 82-90. Morgan Kaufmann, San Francisco, CA, 1998.
-
(1998)
Proc. 15th International Conf. on Alachine Learning
, pp. 82-90
-
-
Bradley, P.S.1
Mangasarian, O.L.2
-
7
-
-
0036161011
-
Choosing Multiple Parameters for Support Vector Machines
-
O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 46(1-3):131-159, 2002.
-
(2002)
Machine Learning
, vol.46
, Issue.1-3
, pp. 131-159
-
-
Chapelle, O.1
Vapnik, V.2
Bousquet, O.3
Mukherjee, S.4
-
8
-
-
0024771664
-
Orthogonal Least Squares and Their Application to Non-linear System Identification
-
S. Chen, S.A. Billings, and W. Luo. Orthogonal Least Squares and Their Application to Non-linear System Identification. International Journal of Control, 50:1873-1896, 1989.
-
(1989)
International Journal of Control
, vol.50
, pp. 1873-1896
-
-
Chen, S.1
Billings, S.A.2
Luo, W.3
-
10
-
-
0003922190
-
-
John Wiley and Sons, New York, USA, second edition
-
R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley and Sons, New York, USA, second edition, 2001.
-
(2001)
Pattern Classification
-
-
Duda, R.O.1
Hart, P.E.2
Stork, D.G.3
-
11
-
-
3242708140
-
Least angle regression
-
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani. Least angle regression. Annals of Statistics, 32(2):407-499, 2004.
-
(2004)
Annals of Statistics
, vol.32
, Issue.2
, pp. 407-499
-
-
Efron, B.1
Hastie, T.2
Johnstone, I.3
Tibshirani, R.4
-
12
-
-
33745321449
-
A Feature Selection Newton Method for Support Vector Machine Classification
-
G. Fung and O. L. Mangasarian. A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Aplications, pages 1-18, 2003.
-
(2003)
Computational Optimization and Aplications
, pp. 1-18
-
-
Fung, G.1
Mangasarian, O.L.2
-
13
-
-
34047096182
-
Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms
-
Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, MIT Press, Cambridge, MA
-
C. Gentile. Fast Feature Selection from Microarray Expression Data via Multiplicative Large Margin Algorithms. In Sebastian Thrun, Lawrence Saul, and Bernhard Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
-
(2004)
Advances in Neural Information Processing Systems 16
-
-
Gentile, C.1
-
14
-
-
84899001870
-
Adaptive Scaling for Feature Selection in SVMs
-
S. Thrun S. Becker and K. Obermayer, editors, Cambridge, MA, USA, MIT Press
-
Y. Grandvalet and S. Canu. Adaptive Scaling for Feature Selection in SVMs. In S. Thrun S. Becker and K. Obermayer, editors, Advances in Neural Information Processing Systems, volume 15, Cambridge, MA, USA, 2003. MIT Press.
-
(2003)
Advances in Neural Information Processing Systems
, vol.15
-
-
Grandvalet, Y.1
Canu, S.2
-
16
-
-
0036161259
-
Gene Selection for Cancer Classification using Support Vector Machines
-
January
-
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning, 40:389-422, January 2002.
-
(2002)
Machine Learning
, vol.40
, pp. 389-422
-
-
Guyon, I.1
Weston, J.2
Barnhill, S.3
Vapnik, V.4
-
17
-
-
22944456331
-
Gene Selection for Microarray Data
-
B. Schölkopf, K. Tsuda, and J.-P. Vert, editors, MIT Press, Cambridge, Massachusetts
-
S. Hochreiter and K. Obermayer. Gene Selection for Microarray Data. In B. Schölkopf, K. Tsuda, and J.-P. Vert, editors, Kernel Methods in Computational Biology. MIT Press, Cambridge, Massachusetts, 2004.
-
(2004)
Kernel Methods in Computational Biology
-
-
Hochreiter, S.1
Obermayer, K.2
-
18
-
-
0008312225
-
Maximum Entropy Discrimination
-
Technical Report AITR-1668, Massachusetts Institute of Technology, Artificial Intelligence Laboratory
-
T. Jaakkola, M. Meila, and T. Jebara. Maximum Entropy Discrimination. Technical Report AITR-1668, Massachusetts Institute of Technology, Artificial Intelligence Laboratory, 1999.
-
(1999)
-
-
Jaakkola, T.1
Meila, M.2
Jebara, T.3
-
21
-
-
84969342575
-
The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant
-
New York, USA, ACM Press
-
J. Kivinen and M. Warmuth. The Perceptron Algorithm vs. Winnow: Linear vs. Logarithmic Mistake Bounds when few Input Variables are Relevant. In Proceedings of the eighth annual conference on Computational learning theory, pages 289-296, New York, USA, 1995. ACM Press.
-
(1995)
Proceedings of the eighth annual conference on Computational learning theory
, pp. 289-296
-
-
Kivinen, J.1
Warmuth, M.2
-
22
-
-
22944474245
-
Gene Expression Analysis: Joint Feature Selection and Classifier Design
-
B. Schölkopf, K. Tsuda, and J.-P. Vert, editors, MIT Press, Cambridge, MA
-
B. Krishnapuram, L. Carin, and A. Hartemink. Gene Expression Analysis: Joint Feature Selection and Classifier Design. In B. Schölkopf, K. Tsuda, and J.-P. Vert, editors, Kernel Methods in Computational Biology. MIT Press, Cambridge, MA, 2004.
-
(2004)
Kernel Methods in Computational Biology
-
-
Krishnapuram, B.1
Carin, L.2
Hartemink, A.3
-
23
-
-
2442670435
-
Support Vector Channel Selection in BCI
-
June
-
T.N. Lal, M. Schröder, T. Hinterberger, J. Weston, M. Bogdan, N. Birbaumer, and B. Schölkopf. Support Vector Channel Selection in BCI. IEEE Transactions on Biomedical Engineering. Special Issue on Brain-Computer Interfaces, 51(6): 1003-1010, June 2001.
-
(2001)
IEEE Transactions on Biomedical Engineering. Special Issue on Brain-Computer Interfaces
, vol.51
, Issue.6
, pp. 1003-1010
-
-
Lal, T.N.1
Schröder, M.2
Hinterberger, T.3
Weston, J.4
Bogdan, M.5
Birbaumer, N.6
Schölkopf, B.7
-
24
-
-
0000494466
-
Optimal Brain Damage
-
D. S. Touretzky, editor, San Mateo, CA, Morgan Kauffman
-
Y. LeCun, J. Denker, S. Solla, R. E. Howard, and L. D. Jackel. Optimal Brain Damage. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems II, San Mateo, CA, 1990. Morgan Kauffman.
-
(1990)
Advances in Neural Information Processing Systems II
-
-
LeCun, Y.1
Denker, J.2
Solla, S.3
Howard, R.E.4
Jackel, L.D.5
-
25
-
-
0013125561
-
Feature Selection with Neural Networks
-
P. Leray and P. Gallinari. Feature Selection with Neural Networks. Behaviormetrika, 26(1), 1999.
-
(1999)
Behaviormetrika
, vol.26
, Issue.1
-
-
Leray, P.1
Gallinari, P.2
-
26
-
-
0036772522
-
Bayesian Automatic Relevance Determination Algorithms for Classifying Gene Expression Data
-
Y. Li, C. Campbell, and M. Tipping. Bayesian Automatic Relevance Determination Algorithms for Classifying Gene Expression Data. Bioinformatics, 18(10):1332-1339, 2002.
-
(2002)
Bioinformatics
, vol.18
, Issue.10
, pp. 1332-1339
-
-
Li, Y.1
Campbell, C.2
Tipping, M.3
-
27
-
-
0003281852
-
On the Estimation of Characters Obtained in Statistical Procedure of Recognition
-
A. Luntz and V. Brailovsky. On the Estimation of Characters Obtained in Statistical Procedure of Recognition. Technicheskaya Kibernetica, 1996.
-
(1996)
Technicheskaya Kibernetica
-
-
Luntz, A.1
Brailovsky, V.2
-
28
-
-
0028698662
-
Bayesian non-linear modelling for the prediction competition
-
D. J. C. MacKay. Bayesian non-linear modelling for the prediction competition. ASHRAE Transactions, 100(2):1053-1062, 1994.
-
(1994)
ASHRAE Transactions
, vol.100
, Issue.2
, pp. 1053-1062
-
-
MacKay, D.J.C.1
-
30
-
-
0000068822
-
A Mathematical Programming Approach to the Kernel Fisher Algorithm
-
S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Cambridge, MA, USA, MIT Press
-
S. Mika, G. Rätsch, and K.-R. Müller. A Mathematical Programming Approach to the Kernel Fisher Algorithm. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems, pages 591-597, Cambridge, MA, USA, 2000. MIT Press.
-
(2000)
Advances in Neural Information Processing Systems
, pp. 591-597
-
-
Mika, S.1
Rätsch, G.2
Müller, K.-R.3
-
31
-
-
0003301456
-
Bayesian Learning for Neural Networks
-
of, Springer
-
R. Neal. Bayesian Learning for Neural Networks, volume 118 of Lecture Notes in Statistics. Springer, 1996.
-
(1996)
Lecture Notes in Statistics
, vol.118
-
-
Neal, R.1
-
32
-
-
84898974506
-
A feature selection algorithm based on the global minimization of a generalization error bound
-
D. Peleg and R. Meir. A feature selection algorithm based on the global minimization of a generalization error bound. In NIPS 18, 2004.
-
(2004)
NIPS 18
-
-
Peleg, D.1
Meir, R.2
-
33
-
-
1942418470
-
Grafting: Fast, Incremental Feature Selection by Gradient Descent in'Function Space
-
S. Perkins, K. Lacker, and J. Theiler. Grafting: Fast, Incremental Feature Selection by Gradient Descent in'Function Space. Journal of Machine Learning Research, 3:1333-1356, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1333-1356
-
-
Perkins, S.1
Lacker, K.2
Theiler, J.3
-
35
-
-
33744584654
-
Induction of Decision Trees
-
J.R. Quinlan. Induction of Decision Trees. Machine Learning, 1(1):81-106, 1986.
-
(1986)
Machine Learning
, vol.1
, Issue.1
, pp. 81-106
-
-
Quinlan, J.R.1
-
37
-
-
0346825283
-
MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling
-
I. Rivals and L. Personnaz. MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling. Journal of Machine Learning Research, 3: 1383-1398, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1383-1398
-
-
Rivals, I.1
Personnaz, L.2
-
39
-
-
84898947199
-
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers
-
S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Cambridge, MA, USA, MIT Press. Spider. Machine Learning Toolbox
-
M. Seeger. Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems, volume 12, Cambridge, MA, USA, 2000. MIT Press. Spider. Machine Learning Toolbox http://www.kyb.tuebingen.mpg.de/bs/ people/spider/, 2004.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
-
-
Seeger, M.1
-
40
-
-
2942701493
-
Ranking a Random Feature for Variable and Feature Selection
-
H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar. Ranking a Random Feature for Variable and Feature Selection. Journal of Machine Learning Research, 3: 1399-1414, 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1399-1414
-
-
Stoppiglia, H.1
Dreyfus, G.2
Dubois, R.3
Oussar, Y.4
-
42
-
-
0001224048
-
Sparse Bayesian Learning and the Relevance Vector Machine
-
M. E. Tipping. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research, 1:211-244, 2001.
-
(2001)
Journal of Machine Learning Research
, vol.1
, pp. 211-244
-
-
Tipping, M.E.1
-
43
-
-
0034264380
-
Bounds on Error Expectation for Support Vector Machines
-
V. Vapnik and O. Chapelle. Bounds on Error Expectation for Support Vector Machines. Neural Computation, 12(9), 2000.
-
(2000)
Neural Computation
, vol.12
, Issue.9
-
-
Vapnik, V.1
Chapelle, O.2
-
45
-
-
0001001098
-
Feature selection for SVMs
-
S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Cambridge, MA, USA, MIT Press
-
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for SVMs. In S.A. Solla, T.K. Leen, and K.-R. Müller, editors, Advances in Neural Information Processing Systems, volume 12, pages 526-532, Cambridge, MA, USA, 2000. MIT Press.
-
(2000)
Advances in Neural Information Processing Systems
, vol.12
, pp. 526-532
-
-
Weston, J.1
Mukherjee, S.2
Chapelle, O.3
Pontil, M.4
Poggio, T.5
Vapnik, V.6
-
46
-
-
84890520049
-
Use of the Zero-Norm with Linear Models and Kernel Methods
-
March
-
J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping. Use of the Zero-Norm with Linear Models and Kernel Methods. Journal of Machine Learning Research, 3: 1439-1461, March 2003.
-
(2003)
Journal of Machine Learning Research
, vol.3
, pp. 1439-1461
-
-
Weston, J.1
Elisseeff, A.2
Schölkopf, B.3
Tipping, M.4
-
48
-
-
0002692783
-
Soft modeling by latent variables; the nonlinear iterative partial least squares approach
-
J. Gani, editor, London, Academic Press
-
H. Wold. Soft modeling by latent variables; the nonlinear iterative partial least squares approach. In J. Gani, editor, Perspectives in Probability and Statistics, Papers in Honours of M.S. Bartlett, London, 1975. Academic Press.
-
(1975)
Perspectives in Probability and Statistics, Papers in Honours of M.S. Bartlett
-
-
Wold, H.1
-
49
-
-
15944363312
-
Classification of Gene Microarrays by Penalized Logistic Regression
-
J. Zhu and T. Hastie. Classification of Gene Microarrays by Penalized Logistic Regression. Bio statistics, 5(3):427-443, 2003.
-
(2003)
Bio statistics
, vol.5
, Issue.3
, pp. 427-443
-
-
Zhu, J.1
Hastie, T.2
|