152 related articles for article (PubMed ID: 17381263)
1. Training a support vector machine in the primal.
Chapelle O
Neural Comput; 2007 May; 19(5):1155-78. PubMed ID: 17381263
[TBL] [Abstract][Full Text] [Related]
2. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines.
Cawley GC; Talbot NL
Neural Netw; 2004 Dec; 17(10):1467-75. PubMed ID: 15541948
[TBL] [Abstract][Full Text] [Related]
3. Theoretical analysis for solution of support vector data description.
Wang X; Chung FL; Wang S
Neural Netw; 2011 May; 24(4):360-9. PubMed ID: 21353975
[TBL] [Abstract][Full Text] [Related]
4. A neural support vector machine.
Jändel M
Neural Netw; 2010 Jun; 23(5):607-13. PubMed ID: 20092978
[TBL] [Abstract][Full Text] [Related]
5. Geometrical properties of nu support vector machines with different norms.
Ikeda K; Murata N
Neural Comput; 2005 Nov; 17(11):2508-29. PubMed ID: 16156937
[TBL] [Abstract][Full Text] [Related]
6. An SMO algorithm for the potential support vector machine.
Knebel T; Hochreiter S; Obermayer K
Neural Comput; 2008 Jan; 20(1):271-87. PubMed ID: 18045009
[TBL] [Abstract][Full Text] [Related]
7. Large-margin classification in infinite neural networks.
Cho Y; Saul LK
Neural Comput; 2010 Oct; 22(10):2678-97. PubMed ID: 20608866
[TBL] [Abstract][Full Text] [Related]
8. Integrating support vector machines and neural networks.
Capparuccia R; De Leone R; Marchitto E
Neural Netw; 2007 Jul; 20(5):590-7. PubMed ID: 17306960
[TBL] [Abstract][Full Text] [Related]
9. Arbitrary norm support vector machines.
Huang K; Zheng D; King I; Lyu MR
Neural Comput; 2009 Feb; 21(2):560-82. PubMed ID: 19431269
[TBL] [Abstract][Full Text] [Related]
10. New support vector algorithms with parametric insensitive/margin model.
Hao PY
Neural Netw; 2010 Jan; 23(1):60-73. PubMed ID: 19716677
[TBL] [Abstract][Full Text] [Related]
11. Recurrent kernel machines: computing with infinite echo state networks.
Hermans M; Schrauwen B
Neural Comput; 2012 Jan; 24(1):104-33. PubMed ID: 21851278
[TBL] [Abstract][Full Text] [Related]
12. A hierarchical classifier using new support vector machines for automatic target recognition.
Casasent D; Wang YC
Neural Netw; 2005; 18(5-6):541-8. PubMed ID: 16087318
[TBL] [Abstract][Full Text] [Related]
13. New delay-dependent exponential stability for neural networks with time delay.
Mou S; Gao H; Qiang W; Chen K
IEEE Trans Syst Man Cybern B Cybern; 2008 Apr; 38(2):571-6. PubMed ID: 18348940
[TBL] [Abstract][Full Text] [Related]
14. A small sphere and large margin approach for novelty detection using training data with outliers.
Wu M; Ye J
IEEE Trans Pattern Anal Mach Intell; 2009 Nov; 31(11):2088-92. PubMed ID: 19762934
[TBL] [Abstract][Full Text] [Related]
15. Generalized synchronization via nonlinear control.
Juan M; Xingyuan W
Chaos; 2008 Jun; 18(2):023108. PubMed ID: 18601475
[TBL] [Abstract][Full Text] [Related]
16. Support vector machines for predictive modeling in heterogeneous catalysis: a comprehensive introduction and overfitting investigation based on two real applications.
Baumes LA; Serra JM; Serna P; Corma A
J Comb Chem; 2006; 8(4):583-96. PubMed ID: 16827571
[TBL] [Abstract][Full Text] [Related]
17. Stability of Cohen-Grossberg neural networks with time-varying delays.
Huang T; Chan A; Huang Y; Cao J
Neural Netw; 2007 Oct; 20(8):868-73. PubMed ID: 17720364
[TBL] [Abstract][Full Text] [Related]
18. A user's guide to support vector machines.
Ben-Hur A; Weston J
Methods Mol Biol; 2010; 609():223-39. PubMed ID: 20221922
[TBL] [Abstract][Full Text] [Related]
19. Neural-network-based decentralized adaptive control for a class of large-scale nonlinear systems with unknown time-varying delays.
Yoo SJ; Park JB
IEEE Trans Syst Man Cybern B Cybern; 2009 Oct; 39(5):1316-23. PubMed ID: 19342350
[TBL] [Abstract][Full Text] [Related]
20. An LMI approach to global asymptotic stability of the delayed Cohen-Grossberg neural network via nonsmooth analysis.
Yu W; Cao J; Wang J
Neural Netw; 2007 Sep; 20(7):810-8. PubMed ID: 17703919
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
