-
1
-
-
0000710299
-
Queries and concept learning
-
D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
-
(1988)
Machine Learning
, vol.2
, pp. 319-342
-
-
Angluin, D.1
-
3
-
-
0043244916
-
BCI bit rates and error detection for fast-pace motor commands based on single-trial EEG analysis
-
B.. Blankertz, G. Dornhege, C. Schäfer, R. Krepki, J. Kohlmorgen, K.-R. Müller, V. Kunzmann, F. Losch, and G. Curio. BCI bit rates and error detection for fast-pace motor commands based on single-trial EEG analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 11:127-131, 2003.
-
(2003)
IEEE Transactions on Neural Systems and Rehabilitation Engineering
, vol.11
, pp. 127-131
-
-
Blankertz, B.1
Dornhege, G.2
Schäfer, C.3
Krepki, R.4
Kohlmorgen, J.5
Müller, K.-R.6
Kunzmann, V.7
Losch, F.8
Curio, G.9
-
4
-
-
25444522689
-
Fast kernel classifiers for online and active learning
-
A. Bordes, S. Ertekin, J. Wesdon, and L. Bottou. Fast kernel classifiers for online and active learning. Journal of Machine Learning Research, 6:1579-1619, 2005.
-
(2005)
Journal of Machine Learning Research
, vol.6
, pp. 1579-1619
-
-
Bordes, A.1
Ertekin, S.2
Wesdon, J.3
Bottou, L.4
-
5
-
-
80052866161
-
Incremental and decremental support vector machine learning
-
T. K. Leen, T. G. Dietterich, and V. Tresp, editors. MIT Press
-
G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13, pages 409-415. MIT Press, 2001.
-
(2001)
Advances in Neural Information Processing Systems
, vol.13
, pp. 409-415
-
-
Cauwenberghs, G.1
Poggio, T.2
-
7
-
-
0004614981
-
Libsvm: Introduction and benchmarks
-
Department of Computer Science and Information Engineering, National Taiwan University, Taipei
-
C.-C. Chang and C.-J. Lin. Libsvm: Introduction and benchmarks. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 2000.
-
(2000)
Technical Report
-
-
Chang, C.-C.1
Lin, C.-J.2
-
8
-
-
0000913324
-
SVM torch: Support vector machines for large-scale regression problems
-
R. Collobert and S. Bengio. SVM Torch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001.
-
(2001)
Journal of Machine Learning Research
, vol.1
, pp. 143-160
-
-
Collobert, R.1
Bengio, S.2
-
9
-
-
84897965802
-
AUC optimization vs. error rate minimization
-
C. Cortes and M. Mohri. AUC optimization vs. error rate minimization. In Proc. NIPS'2003, 2004.
-
(2004)
Proc. NIPS'2003
-
-
Cortes, C.1
Mohri, M.2
-
10
-
-
0141797880
-
Chapter a geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data
-
Kluwer
-
E. Eskin, A. Arnold, M. Prerau, L. Portnoy, and S. Stolfo. Applications of Data Mining in Computer Security, chapter A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. Kluwer, 2002.
-
(2002)
Applications of Data Mining in Computer Security
-
-
Eskin, E.1
Arnold, A.2
Prerau, M.3
Portnoy, L.4
Stolfo, S.5
-
11
-
-
0004236492
-
-
John Hopkins University Press, Baltimore, London, 3rd edition
-
G. H. Golub and C. F. van Loan. Matrix Computations. John Hopkins University Press, Baltimore, London, 3rd edition, 1996.
-
(1996)
Matrix Computations
-
-
Golub, G.H.1
Van Loan, C.F.2
-
12
-
-
0002714543
-
Making large-scale SVM learning practical
-
B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Cambridge, MA. MIT Press
-
T. Joachims. Making large-scale SVM learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods -Support Vector Learning, pages 169-184, Cambridge, MA, 1999. MIT Press.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 169-184
-
-
Joachims, T.1
-
13
-
-
0141444717
-
Online learning with kernels
-
T. G. Diettrich, S. Becker, and Z. Ghahramani, editors
-
J. Kivinen, A. J. Smola, and R. C. Williamson. Online learning with kernels. In T. G. Diettrich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Inf. Proc. Systems (NIPS 01), pages 785-792, 2001.
-
(2001)
Advances in Neural Inf. Proc. Systems (NIPS 01)
, pp. 785-792
-
-
Kivinen, J.1
Smola, A.J.2
Williamson, R.C.3
-
14
-
-
0001849163
-
How good is the simplex algorithm?
-
O. Sisha, editor, Academic Press
-
F. Klee and G. J. Minty. How good is the simplex algorithm? In O. Sisha, editor, Inequalities III, pages 159-175. Academic Press, 1972.
-
(1972)
Inequalities III
, pp. 159-175
-
-
Klee, F.1
Minty, G.J.2
-
15
-
-
0036158636
-
Feasible direction decomposition algorithms for training support vector machines
-
P. Laskov. Feasible direction decomposition algorithms for training support vector machines. Machine Learning, 46:315-349, 2002.
-
(2002)
Machine Learning
, vol.46
, pp. 315-349
-
-
Laskov, P.1
-
16
-
-
85016684916
-
Intrusion detection in unlabeled data with quarter-sphere support vector machines
-
P. Laskov, C. Schäfer, and I. Kotenko. Intrusion detection in unlabeled data with quarter-sphere support vector machines. In Proc. DIMVA, pages 71-82, 2004.
-
(2004)
Proc. DIMVA
, pp. 71-82
-
-
Laskov, P.1
Schäfer, C.2
Kotenko, I.3
-
17
-
-
0001857994
-
Efficient backprop
-
G. Orr and K.-R. Müller, editors, Heidelberg, New York. Springer LNCS
-
Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller. Efficient backprop. In G. Orr and K.-R. Müller, editors, Neural Networks: Tricks of the Trade, volume 1524, pages 9-53, Heidelberg, New York, 1998. Springer LNCS.
-
(1998)
Neural Networks: Tricks of the Trade
, vol.1524
, pp. 9-53
-
-
LeCun, Y.1
Bottou, L.2
Orr, G.B.3
Müller, K.-R.4
-
18
-
-
30244571334
-
On-line learning of linear functions
-
University of California at Santa Cruz, October
-
N. Littlestone, P. M. Long, and M. K. Warmuth. On-line learning of linear functions. Technical Report CRL-91-29, University of California at Santa Cruz, October 1991.
-
(1991)
Technical Report
, vol.CRL-91-29
-
-
Littlestone, N.1
Long, P.M.2
Warmuth, M.K.3
-
19
-
-
0141682928
-
Time-series novelty detection using one-class Support Vector Machines
-
to appear
-
J. Ma and S. Perkins. Time-series novelty detection using one-class Support Vector Machines. In IJCNN, 2003. to appear.
-
(2003)
IJCNN
-
-
Ma, J.1
Perkins, S.2
-
21
-
-
0141556297
-
On-line Support Vector Machines for function approximation
-
Universitat Politècnica de Catalunya, Departement de Llengatges i Sistemes Informàtics
-
M. Martin. On-line Support Vector Machines for function approximation. Technical report, Universitat Politècnica de Catalunya, Departement de Llengatges i Sistemes Informàtics, 2002.
-
(2002)
Technical Report
-
-
Martin, M.1
-
23
-
-
0036592037
-
On-line learning in changing environments with applications in supervised and unsupervised learning
-
N. Murata, M. Kawanabe, A. Ziehe, K.-R. Müller, and S.-I. Amari. On-line learning in changing environments with applications in supervised and unsupervised learning. Neural Networks, 15 (4-6):743-760, 2002.
-
(2002)
Neural Networks
, vol.15
, Issue.4-6
, pp. 743-760
-
-
Murata, N.1
Kawanabe, M.2
Ziehe, A.3
Müller, K.-R.4
Amari, S.-I.5
-
24
-
-
84898987101
-
Adaptive on-line learning in changing environments
-
M. C. Mozer, M. I. Jordan, and T. Petsche, editors. The MIT Press
-
N. Murata, K.-R. Müller, A. Ziehe, and S. i. Amari. Adaptive on-line learning in changing environments. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, volume 9, page 599. The MIT Press, 1997.
-
(1997)
Advances in Neural Information Processing Systems
, vol.9
, pp. 599
-
-
Murata, N.1
Müller, K.-R.2
Ziehe, A.3
Amari, S.I.4
-
25
-
-
0004135065
-
-
G. Orr and K.-R. Müller, editors. Springer LNCS
-
G. Orr and K.-R. Müller, editors. Neural Networks: Tricks of the Trade, volume 1524. Springer LNCS, 1998.
-
(1998)
Neural Networks: Tricks of the Trade
, vol.1524
-
-
-
26
-
-
0003120218
-
Fast training of support vector machines using sequential minimal optimization
-
B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors Cambridge, MA,. MIT Press
-
J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, pages 185-208, Cambridge, MA, 1999. MIT Press.
-
(1999)
Advances in Kernel Methods - Support Vector Learning
, pp. 185-208
-
-
Platt, J.1
-
27
-
-
84958962423
-
Incremental support vector machine learning: A local approach
-
L. Ralaivola and F. d'Alché Buc. Incremental support vector machine learning: A local approach. Lecture Notes in Computer Science, 2130:322-329, 2001.
-
(2001)
Lecture Notes in Computer Science
, vol.2130
, pp. 322-329
-
-
Ralaivola, L.1
D'Alché Buc, F.2
-
28
-
-
0000016172
-
A stochastic approximation method
-
H. Robbins and S. Munro. A stochastic approximation method. Ann. Math. Stat., 22:400-407, 1951.
-
(1951)
Ann. Math. Stat.
, vol.22
, pp. 400-407
-
-
Robbins, H.1
Munro, S.2
-
29
-
-
33646351867
-
Incremental learning with support vector machines
-
Universität Dortmund, SFB475
-
S. Rüping. Incremental learning with support vector machines. Technical Report TR-18, Universität Dortmund, SFB475, 2002.
-
(2002)
Technical Report
, vol.TR-18
-
-
Rüping, S.1
-
30
-
-
0004069068
-
-
D. Saad, editor. Cambridge University Press
-
D. Saad, editor. On-line learning in neural networks. Cambridge University Press, 1998.
-
(1998)
On-line Learning in Neural Networks
-
-
-
31
-
-
0032594954
-
Input space vs. feature space in kernel-based methods
-
September
-
B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K.-R. Müller, G. Ratsch, and A. J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5): 1000-1017, September 1999.
-
(1999)
IEEE Transactions on Neural Networks
, vol.10
, Issue.5
, pp. 1000-1017
-
-
Schölkopf, B.1
Mika, S.2
Burges, C.J.C.3
Knirsch, P.4
Müller, K.-R.5
Ratsch, G.6
Smola, A.J.7
-
32
-
-
0000487102
-
Estimating the support of a high-dimensional distribution
-
B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson. Estimating the support of a high-dimensional distribution. Neural Computation, 13(7):1443-1471, 2001.
-
(2001)
Neural Computation
, vol.13
, Issue.7
, pp. 1443-1471
-
-
Schölkopf, B.1
Platt, J.2
Shawe-Taylor, J.3
Smola, A.J.4
Williamson, R.C.5
-
34
-
-
0001986205
-
Data domain description by support vectors
-
M. Verleysen, editor Brussels,. D. Facto Press
-
D. Tax and R. Duin. Data domain description by support vectors. In M. Verleysen, editor, Proc. ESANN, pages 251-256, Brussels, 1999. D. Facto Press.
-
(1999)
Proc. ESANN
, pp. 251-256
-
-
Tax, D.1
Duin, R.2
-
35
-
-
79960753941
-
Online SVM learning: From classification to data description and back
-
C. et al. Molina, editor
-
D. M. J. Tax and P. Laskov. Online SVM learning: from classification to data description and back. In C. et al. Molina, editor, Proc. NNSP, pages 499-508, 2003.
-
(2003)
Proc. NNSP
, pp. 499-508
-
-
Tax, D.M.J.1
Laskov, P.2
-
38
-
-
0037365194
-
Support Vector Machines for active learning in the drug discovery process
-
M. K. Warmuth, J. Liao, G. Rätsch, M. Mathieson, S. Putta, and C. Lemmem. Support Vector Machines for active learning in the drug discovery process. Journal of Chemical Information Sciences, 43(2):667-673, 2003.
-
(2003)
Journal of Chemical Information Sciences
, vol.43
, Issue.2
, pp. 667-673
-
-
Warmuth, M.K.1
Liao, J.2
Rätsch, G.3
Mathieson, M.4
Putta, S.5
Lemmem, C.6
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