-
1
-
-
0029404270
-
An unsupervised, on-line system for induction motor fault detection using Stator current monitoring
-
Schoen R.R., Lin B.K., Habetler T.G., Schlag J.H., and Farag S. An unsupervised, on-line system for induction motor fault detection using Stator current monitoring. IEEE Trans. Industry Appl. 31 6 (1995) 1274-1279
-
(1995)
IEEE Trans. Industry Appl.
, vol.31
, Issue.6
, pp. 1274-1279
-
-
Schoen, R.R.1
Lin, B.K.2
Habetler, T.G.3
Schlag, J.H.4
Farag, S.5
-
2
-
-
0036875990
-
Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics
-
Tallam R.M., Habetler T.G., and Harley R.G. Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics. IEEE Trans. Power Electronics 17 6 (2002)
-
(2002)
IEEE Trans. Power Electronics
, vol.17
, Issue.6
-
-
Tallam, R.M.1
Habetler, T.G.2
Harley, R.G.3
-
3
-
-
0035425745
-
A multi-agent system for monitoring industrial gas turbine start-up sequences
-
Mangina E.E., McArthur S.D.J., McDonald J.R., and Moyes A. A multi-agent system for monitoring industrial gas turbine start-up sequences. IEEE Trans. on Power Systems 16 3 (2001) 396-401
-
(2001)
IEEE Trans. on Power Systems
, vol.16
, Issue.3
, pp. 396-401
-
-
Mangina, E.E.1
McArthur, S.D.J.2
McDonald, J.R.3
Moyes, A.4
-
9
-
-
0001913553
-
An introduction to kernel methods
-
Howlett R.J., and Jain L.C. (Eds), Berlin, Physica-Verlag
-
Campbell C. An introduction to kernel methods. In: Howlett R.J., and Jain L.C. (Eds). Radial Basis Function Networks: Design and Applications (2000), Berlin, Physica-Verlag 155-192
-
(2000)
Radial Basis Function Networks: Design and Applications
, pp. 155-192
-
-
Campbell, C.1
-
10
-
-
0004094721
-
-
MIT press, Cambridge, MA, USA
-
Smola A., and Schoelkopf B. Learning with Kernels (2002), MIT press, Cambridge, MA, USA
-
(2002)
Learning with Kernels
-
-
Smola, A.1
Schoelkopf, B.2
-
12
-
-
0036709275
-
Constructing boosting algorithms from SVMs: an application to one-class classification
-
Raetsch G., Mika S., Schoelkopf B., and Mueller K.-R. Constructing boosting algorithms from SVMs: an application to one-class classification. IEEE Trans. Pattern Anal. Machine Intell. 24 9 (2002) 1184-1199
-
(2002)
IEEE Trans. Pattern Anal. Machine Intell.
, vol.24
, Issue.9
, pp. 1184-1199
-
-
Raetsch, G.1
Mika, S.2
Schoelkopf, B.3
Mueller, K.-R.4
-
13
-
-
33646941290
-
-
N. Smith, M. Gales, M. Niranjan, Data-dependent kernels in SVM classification of speech patterns, Technical Report CUED/F-INFENG/TR.387, Engineering Department, University of Cambridge, UK, April 2001.
-
-
-
-
14
-
-
33646909908
-
-
P. Hayton, B. Schœlkopf, L. Tarassenko, P. Anuzis, Support vector novelty detection applied to jet engine vibration spectra, in: NIPS'2000, 2000.
-
-
-
-
16
-
-
33646920625
-
-
B. Schoelkopf, J. Platt, J. Shaw-Taylor, A. Smola, R.C. Williamson, Estimating the support of a high-dimensional distribution. Technical Report TR87, Microsoft Research, Redmond, WA, USA, 1999.
-
-
-
-
17
-
-
80052866161
-
-
G. Cauwenberghs, T. Poggio, Incremental and decremental support vector machine learning, in: Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, 2001.
-
-
-
-
19
-
-
0003462953
-
-
Wiley, Berlin
-
Van H.L. Trees, Detection, Estimation, and Modulation Theory (1968), Wiley, Berlin
-
(1968)
Trees, Detection, Estimation, and Modulation Theory
-
-
Van, H.L.1
-
21
-
-
33646924841
-
-
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in: IJCAI, 1995, pp. 1137-1145.
-
-
-
-
23
-
-
33646925375
-
-
A. Gretton, R. Herbrich, B. Schoelkopf, A.J. Smola, P.J.W. Rayner, Bound on the leave-one-out error for density support estimation using ν-SVMs, Technical Report, University of Cambridge Engineering Department, 2001.
-
-
-
-
24
-
-
33646938736
-
-
A. Gretton, Kernel methods for classification and signal separation, Ph.D. Thesis, University of Cambridge, Cambridge, UK, 2003.
-
-
-
-
25
-
-
0036959150
-
Optimised support vector machines for nonstationary signal classification
-
Davy M., Gretton A., Doucet A., and Rayner P.J.W. Optimised support vector machines for nonstationary signal classification. Signal Process. Lett. 9 12 (2002)
-
(2002)
Signal Process. Lett.
, vol.9
, Issue.12
-
-
Davy, M.1
Gretton, A.2
Doucet, A.3
Rayner, P.J.W.4
-
26
-
-
33646910506
-
-
M. Davy, S. Godsill, Detection of abrupt spectral changes using support vector machines. An application to audio signal segmentation, in: IEEE ICASSP-02, Orlando, USA, May 2002.
-
-
-
-
27
-
-
33646951015
-
-
N. Smith, M. Gales, Using SVMs and discriminative models for speech recognition, in: IEEE ICASSP-02, Orlando, USA, May 2002.
-
-
-
-
29
-
-
84898940321
-
-
J. Kivinen, A.J. Smola, R.C. Williamson, Online learning with kernels, in: T.G. Dietterich, S. Becker, Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems, vol. 14, MIT Press, Cambridge, MA, 2002.
-
-
-
-
30
-
-
33750141933
-
-
H. A. Boubacar, S. Lecoeuche, S. Maouche, Self adaptive kernel machine: online clustering in RKHS, in: International Joint Conférence on Neural Networks, Montreal, Canada, July 2005.
-
-
-
-
31
-
-
33646931201
-
-
S.V.N. Vishwanathan, A. Smola, N. Narasimba Murty, Ssvm: a simple svm algorithm, in: ICML, Washington, DC USA, August 2003.
-
-
-
-
32
-
-
33646935727
-
-
F. Desobry, M. Davy, Support vector-based detection of abrupt changes, in: IEEE ICASSP-03, Hong-Kong, China, April 2003.
-
-
-
|