-
1
-
-
0026966646
-
A training algorithm for optimal margin classifiers
-
ACM Press, New York
-
Boser, B., Guyon, I., Vapnik, V.: A Training Algorithm for Optimal Margin Classifiers. Fifth Annual Workshop on Computational Learning Theory. ACM Press, New York. 1992
-
(1992)
Fifth Annual Workshop on Computational Learning Theory
-
-
Boser, B.1
Guyon, I.2
Vapnik, V.3
-
2
-
-
34249753618
-
Support vector networks
-
Cortes, C., Vapnik, V.: Support Vector Networks. Machine learning. 20 (1995) 273-297
-
(1995)
Machine Learning
, vol.20
, pp. 273-297
-
-
Cortes, C.1
Vapnik, V.2
-
4
-
-
84898937307
-
Support vector method for multivariate density estimation
-
MIT Press, Cambridge, MA
-
Vapnik, V. et al.: Support Vector Method for Multivariate Density Estimation. Advances in Neural Information Processing System. MIT Press, Cambridge, MA. 12 (1999) 659-665
-
(1999)
Advances in Neural Information Processing System
, vol.12
, pp. 659-665
-
-
Vapnik, V.1
-
5
-
-
26844550191
-
Regression using support vector machines: Basic foundations
-
Electrical and Computer Engineering Department, University of Louisville
-
Aly, F., Refaat, M.: Regression Using Support Vector Machines: Basic Foundations. Technical Report. Electrical and Computer Engineering Department, University of Louisville. 2004
-
(2004)
Technical Report
-
-
Aly, F.1
Refaat, M.2
-
7
-
-
2942746029
-
Learning and soft computing: Support vector machines
-
MIT Press, Cambridge, MA
-
Kecman, V.: Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models. MIT Press, Cambridge, MA. 2001
-
(2001)
Neural Networks, and Fuzzy Logic Models
-
-
Kecman, V.1
-
8
-
-
1842430977
-
Sparse modeling using orthogonal forward regression with PRESS statistic and regularization
-
Chen, S. et al.: Sparse Modeling Using Orthogonal forward Regression with PRESS Statistic and Regularization. IEEE Trans. on Systems, Man and Cybernetics, Part B. 34 (2004) 898-911
-
(2004)
IEEE Trans. on Systems, Man and Cybernetics, Part B
, vol.34
, pp. 898-911
-
-
Chen, S.1
-
9
-
-
0032786569
-
Improving support vector machine classifiers by modifying kernel functions
-
Amari, S., Wu, S.: Improving Support Vector Machine Classifiers by Modifying Kernel Functions. Neural Networks. 12 (1999) 783-789
-
(1999)
Neural Networks
, vol.12
, pp. 783-789
-
-
Amari, S.1
Wu, S.2
-
10
-
-
0036469481
-
Conformal transformation of kernel functions: A data-dependent way to improve support vector machine classifiers
-
Wu, S., Amari, S.: Conformal Transformation of Kernel Functions: a Data-dependent Way to Improve Support Vector Machine Classifiers. Neural Processing Letters. 15 (2002) 59-67
-
(2002)
Neural Processing Letters
, vol.15
, pp. 59-67
-
-
Wu, S.1
Amari, S.2
-
11
-
-
0036825821
-
Kernel methods: A survey of current techniques
-
Colin, C.: Kernel Methods: A Survey of Current Techniques. Neurocomputing. 48 (2002) 63-48
-
(2002)
Neurocomputing
, vol.48
, pp. 63-148
-
-
Colin, C.1
-
13
-
-
4444224442
-
Support vector machines regression on-line modelling and its application
-
Wang, D.C.: Support Vector Machines Regression On-line Modelling and Its Application (In Chinese). Control and Decision. 18 (2003) 89-91
-
(2003)
Control and Decision
, vol.18
, pp. 89-91
-
-
Wang, D.C.1
-
14
-
-
3442887043
-
A real-time model for forecasting zinc output by support vector machines in imperial smelting furnace
-
Kunzhi, H.: A Real-time Model for Forecasting Zinc Output by Support Vector Machines in Imperial Smelting Furnace (In Chinese). Computer Engineering. 30 (2004) 16-18
-
(2004)
Computer Engineering
, vol.30
, pp. 16-18
-
-
Kunzhi, H.1
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