These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
316 related articles for article (PubMed ID: 20123572)
21. Low rank updated LS-SVM classifiers for fast variable selection. Ojeda F; Suykens JA; De Moor B Neural Netw; 2008; 21(2-3):437-49. PubMed ID: 18343309 [TBL] [Abstract][Full Text] [Related]
22. Confidence-based active learning. Li M; Sethi IK IEEE Trans Pattern Anal Mach Intell; 2006 Aug; 28(8):1251-61. PubMed ID: 16886861 [TBL] [Abstract][Full Text] [Related]
24. Semisupervised least squares support vector machine. Adankon MM; Cheriet M; Biem A IEEE Trans Neural Netw; 2009 Dec; 20(12):1858-70. PubMed ID: 19963446 [TBL] [Abstract][Full Text] [Related]
25. A bottom-up method for simplifying support vector solutions. Nguyen D; Ho T IEEE Trans Neural Netw; 2006 May; 17(3):792-6. PubMed ID: 16722181 [TBL] [Abstract][Full Text] [Related]
26. Bounds on error expectation for support vector machines. Vapnik V; Chapelle O Neural Comput; 2000 Sep; 12(9):2013-36. PubMed ID: 10976137 [TBL] [Abstract][Full Text] [Related]
27. A novel approach for short-term load forecasting using support vector machines. Tian L; Noore A Int J Neural Syst; 2004 Oct; 14(5):329-35. PubMed ID: 15593381 [TBL] [Abstract][Full Text] [Related]
28. Classification of electrocardiogram signals with support vector machines and particle swarm optimization. Melgani F; Bazi Y IEEE Trans Inf Technol Biomed; 2008 Sep; 12(5):667-77. PubMed ID: 18779082 [TBL] [Abstract][Full Text] [Related]
29. Associative memory design using support vector machines. Casali D; Costantini G; Perfetti R; Ricci E IEEE Trans Neural Netw; 2006 Sep; 17(5):1165-74. PubMed ID: 17001978 [TBL] [Abstract][Full Text] [Related]
30. Nonlinear regularization path for quadratic loss support vector machines. Karasuyama M; Takeuchi I IEEE Trans Neural Netw; 2011 Oct; 22(10):1613-25. PubMed ID: 21880570 [TBL] [Abstract][Full Text] [Related]
31. Support vector echo-state machine for chaotic time-series prediction. Shi Z; Han M IEEE Trans Neural Netw; 2007 Mar; 18(2):359-72. PubMed ID: 17385625 [TBL] [Abstract][Full Text] [Related]
32. Determination of global minima of some common validation functions in support vector machine. Yang JB; Ong CJ IEEE Trans Neural Netw; 2011 Apr; 22(4):654-9. PubMed ID: 21342841 [TBL] [Abstract][Full Text] [Related]
33. A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Auer P; Burgsteiner H; Maass W Neural Netw; 2008 Jun; 21(5):786-95. PubMed ID: 18249524 [TBL] [Abstract][Full Text] [Related]
34. An improvement of extreme learning machine for compact single-hidden-layer feedforward neural networks. Huynh HT; Won Y; Kim JJ Int J Neural Syst; 2008 Oct; 18(5):433-41. PubMed ID: 18991365 [TBL] [Abstract][Full Text] [Related]
35. Regularized Least Squares Cancer classifiers from DNA microarray data. Ancona N; Maglietta R; D'Addabbo A; Liuni S; Pesole G BMC Bioinformatics; 2005 Dec; 6 Suppl 4(Suppl 4):S2. PubMed ID: 16351746 [TBL] [Abstract][Full Text] [Related]
36. Novel maximum-margin training algorithms for supervised neural networks. Ludwig O; Nunes U IEEE Trans Neural Netw; 2010 Jun; 21(6):972-84. PubMed ID: 20409990 [TBL] [Abstract][Full Text] [Related]