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.
72 related articles for article (PubMed ID: 18263010)
1. Reorganizing knowledge in neural networks: an explanatory mechanism for neural networks in data classification problems. Narazaki H; Watanabe T; Yamamoto M IEEE Trans Syst Man Cybern B Cybern; 1996; 26(1):107-17. PubMed ID: 18263010 [TBL] [Abstract][Full Text] [Related]
2. Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Yang J; Singh H; Hines EL; Schlaghecken F; Iliescu DD; Leeson MS; Stocks NG Artif Intell Med; 2012 Jun; 55(2):117-26. PubMed ID: 22503644 [TBL] [Abstract][Full Text] [Related]
3. Optimal design of connectivity in neural network training. Jordanov I; Brown R Biomed Sci Instrum; 2000; 36():27-32. PubMed ID: 10834204 [TBL] [Abstract][Full Text] [Related]
4. The Chebyshev-polynomials-based unified model neural networks for function approximation. Lee TT; Jeng JT IEEE Trans Syst Man Cybern B Cybern; 1998; 28(6):925-35. PubMed ID: 18256014 [TBL] [Abstract][Full Text] [Related]
5. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. Ishibuchi H; Nakashima T; Murata T IEEE Trans Syst Man Cybern B Cybern; 1999; 29(5):601-18. PubMed ID: 18252338 [TBL] [Abstract][Full Text] [Related]
6. Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming. Barton AJ; Valdés JJ; Orchard R Neural Netw; 2009; 22(5-6):614-22. PubMed ID: 19604672 [TBL] [Abstract][Full Text] [Related]
7. Function approximation based on fuzzy rules extracted from partitioned numerical data. Thawonmas R; Abe S IEEE Trans Syst Man Cybern B Cybern; 1999; 29(4):525-34. PubMed ID: 18252327 [TBL] [Abstract][Full Text] [Related]
8. Use of a Deep Belief Network for Small High-Level Abstraction Data Sets Using Artificial Intelligence with Rule Extraction. Hayashi Y Neural Comput; 2018 Dec; 30(12):3309-3326. PubMed ID: 30314421 [TBL] [Abstract][Full Text] [Related]
10. Greedy rule generation from discrete data and its use in neural network rule extraction. Odajima K; Hayashi Y; Tianxia G; Setiono R Neural Netw; 2008 Sep; 21(7):1020-8. PubMed ID: 18442894 [TBL] [Abstract][Full Text] [Related]
11. Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: a case study on renal function evaluation. Song Q; Kasabov N; Ma T; Marshall MR Artif Intell Med; 2006 Mar; 36(3):235-44. PubMed ID: 16213694 [TBL] [Abstract][Full Text] [Related]
12. Selective information enhancement learning for creating interpretable representations in competitive learning. Kamimura R Neural Netw; 2011 May; 24(4):387-405. PubMed ID: 21300520 [TBL] [Abstract][Full Text] [Related]
14. Genetic design of biologically inspired receptive fields for neural pattern recognition. Perez CA; Salinas CA; Estevez PA; Valenzuela PM IEEE Trans Syst Man Cybern B Cybern; 2003; 33(2):258-70. PubMed ID: 18238176 [TBL] [Abstract][Full Text] [Related]
15. Fuzzy classifications using fuzzy inference networks. Cai LY; Kwan HK IEEE Trans Syst Man Cybern B Cybern; 1998; 28(3):334-47. PubMed ID: 18255951 [TBL] [Abstract][Full Text] [Related]
16. Providing understanding of the behavior of feedforward neural networks. Huang SH; Endsley MR IEEE Trans Syst Man Cybern B Cybern; 1997; 27(3):465-74. PubMed ID: 18255885 [TBL] [Abstract][Full Text] [Related]
17. On classification capability of neural networks: a case study with otoneurological data. Juhola M; Viikki K; Laurikkala J; Pyykkö I; Kentala E Stud Health Technol Inform; 2001; 84(Pt 1):474-8. PubMed ID: 11604785 [TBL] [Abstract][Full Text] [Related]
19. The use of artificial neural networks in biomedical technologies: an introduction. Alvager T; Smith TJ; Vijai F Biomed Instrum Technol; 1994; 28(4):315-22. PubMed ID: 7920848 [TBL] [Abstract][Full Text] [Related]
20. On the classification capability of sign-constrained perceptrons. Legenstein R; Maass W Neural Comput; 2008 Jan; 20(1):288-309. PubMed ID: 18045010 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]