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Results: 1-16 |
Results: 16
Training invariant support vector machines
Authors: Decoste, D Scholkopf, B
Citation: D. Decoste et B. Scholkopf, Training invariant support vector machines, MACH LEARN, 46(1-3), 2002, pp. 161-190
An introduction to kernel-based learning algorithms
Authors: Muller, KR Mika, S Ratsch, G Tsuda, K Scholkopf, B
Citation: Kr. Muller et al., An introduction to kernel-based learning algorithms, IEEE NEURAL, 12(2), 2001, pp. 181-201
Estimating the support of a high-dimensional distribution
Authors: Scholkopf, B Platt, JC Shawe-Taylor, J Smola, AJ Williamson, RC
Citation: B. Scholkopf et al., Estimating the support of a high-dimensional distribution, NEURAL COMP, 13(7), 2001, pp. 1443-1471
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators
Authors: Williamson, RC Smola, AJ Scholkopf, B
Citation: Rc. Williamson et al., Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators, IEEE INFO T, 47(6), 2001, pp. 2516-2532
Engineering support vector machine kernels that recognize translation initiation sites
Authors: Zien, A Ratsch, G Mika, S Scholkopf, B Lengauer, T Muller, KR
Citation: A. Zien et al., Engineering support vector machine kernels that recognize translation initiation sites, BIOINFORMAT, 16(9), 2000, pp. 799-807
Introduction to large margin classifiers
Authors: Smola, AJ Bartlett, PL Scholkopf, B Schuurmans, D
Citation: Aj. Smola et al., Introduction to large margin classifiers, ADV NEUR IN, 2000, pp. 1-29
Natural regularization from generative models
Authors: Oliver, N Scholkopf, B Smola, AJ
Citation: N. Oliver et al., Natural regularization from generative models, ADV NEUR IN, 2000, pp. 51-60
Robust ensemble learning
Authors: Ratsch, G Scholkopf, B Smola, AJ Mika, S Onoda, T Muller, KR
Citation: G. Ratsch et al., Robust ensemble learning, ADV NEUR IN, 2000, pp. 207-220
Entropy numbers for convex combinations and MLPs
Authors: Smola, AJ Elisseeff, A Scholkopf, B Williamson, RC
Citation: Aj. Smola et al., Entropy numbers for convex combinations and MLPs, ADV NEUR IN, 2000, pp. 369-387
New support vector algorithms
Authors: Scholkopf, B Smola, AJ Williamson, RC Bartlett, PL
Citation: B. Scholkopf et al., New support vector algorithms, NEURAL COMP, 12(5), 2000, pp. 1207-1245
Input space versus feature space in kernel-based methods
Authors: Scholkopf, B Mika, S Burges, CJC Knirsch, P Muller, KR Ratsch, G Smola, AJ
Citation: B. Scholkopf et al., Input space versus feature space in kernel-based methods, IEEE NEURAL, 10(5), 1999, pp. 1000-1017
Advances in kernel methods - Support vector learning - Introduction
Authors: Scholkopf, B Burges, CJC Smola, AJ
Citation: B. Scholkopf et al., Advances in kernel methods - Support vector learning - Introduction, ADVANCES IN KERNEL METHODS, 1999, pp. 1-15
Entropy numbers, operators and support vector kernels
Authors: Williamson, RC Smola, AJ Scholkopf, B
Citation: Rc. Williamson et al., Entropy numbers, operators and support vector kernels, ADVANCES IN KERNEL METHODS, 1999, pp. 127-144
Using support vector machines for time series prediction
Authors: Muller, KR Smola, AJ Ratsch, G Scholkopf, B Kohlmorgen, J Vapnik, V
Citation: Kr. Muller et al., Using support vector machines for time series prediction, ADVANCES IN KERNEL METHODS, 1999, pp. 243-253
Kernel principle component analysis
Authors: Scholkopf, B Smola, AJ Muller, KR
Citation: B. Scholkopf et al., Kernel principle component analysis, ADVANCES IN KERNEL METHODS, 1999, pp. 327-352
The moon tilt illusion
Authors: Scholkopf, B
Citation: B. Scholkopf, The moon tilt illusion, PERCEPTION, 27(10), 1998, pp. 1229-1232
Risultati: 1-16 |