A vector gravitational force model for classification

Pattern Analysis and Applications

Volumn 11, Issue 2, 2008, Pages 169-177

  Zong Chang, Yang ^a,b  

^a HUNAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Classification; Vector gravitational force model; Vector gravitational recognition

Indexed keywords

EID: 44449107130     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-007-0091-9     Document Type: Article

References (29)

1. Duda RO, Hart PE, Stork DG (2004) Pattern classification, 2nd edn. Wiley, New York. ISBN 0-471-05669-3
2. Theodoridis S, Koutroumbas K (2003) Pattern recognition, 2nd edn. Elsevier, Amsterdam. ISBN 0-12-685876-6
3. Haykin S (2001) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall IncEnglewood Cliffs. ISBN 7-302-04936-X
4. Schreiber I, Ben-Bassat M (1990) Polygonal object recognition. Pattern Recognition, 1990. In: Proceedings, 10th international conference on 16-21 June 1990, vol 1,l pp 852-859
5. Chen SG, Wu JY (2005) A geometric interpretation of weighted normal vectors and its improvements. In: Computer graphics, imaging and vision: new trends, international conference on 26-29 July 2005, pp 422-425
6. Brandstein M, Ward D (eds) (2001) Microphone arrays. Springer, Heidelberg
7. Markou M, Singh S (2004) Feature selection based on a Black Hole model of data reorganization. In: 17th International conference on pattern recognition (ICPR04), Cambridge, pp 565-568
8. Singh S, Markou M (2005) A black hole novelty detector for video analysis. Pattern Anal Appl 8:102-114
9. Endo Y, Iwata H (2005) Dynamic clustering based on universal gravitation model. Lect Notes Comput Sci 3558:183-193
10. Wang C, Chen YQ (2005) Improving nearest neighbor classification with simulated gravitational collapse. Lect Notes Comput Sci 3612:845-854
11. Kita Y (1996) Force-based registration method using attribute values. In: Pattern recognition, proceedings of the 13th international conference on 25-29 Aug 1996, vol 2, pp 34-39
12. Cariou C, Chehdi K, Nagle A (2005) Gravitational transform for data clustering-application to multicomponent image classification. In: Acoustics, speech, and signal processing (ICASSP 2005), IEEE International Conference on March 18-23, 2005, vol 2, pp 105-108
13. UCI Machine Learning (2006) Machine-learning-databases. ftp://ftp.ics.uci.edu/pub/machine-learning-databases, 2006. ( http://www.ics.uci.edu/~mlearn/databases, 2006 )
14. Spiehler V (1987) BEAGLE(Her rule-based system). DABFT Diagnostic Products Corporation
15. Xu Y, Zhang X (2005) Glass identification. http://www.stat.ohio-state. edu/~goel//STATLEARN/PROJECTS/05Presentations/Xu_ZhangPresentation.pdf
16. Hechenbichler K, Schliep KP (2004) Weighted k-nearest-neighbor techniques and ordinal classification. Discussion Paper 399, SFB 386, Ludwig-Maximilians University Munich
17. Hechenbichler K (2005) Ensemble-Techniken und ordinale Klassifikation. PhD Thesis, Germany
18. Domeniconi C et al (2002) Locally adaptive metric nearest neighbor classification. IEEE Trans Pattern Anal Mach Intell 24(9):1281-1285
19. Gorzalczany MB, Gradzlu P (2000) A neuro-fuzzy-genetic classifier for technical applications. In: Proceedings of IEEE international conference on industrial technology 19-22, Jan. 2000, vol 2, pp 503-508
20. Tin J, Kwok Y (1999) Moderating the outputs of support vector machine classifiers. IEEE Trans NN 10(5):1018-1031
21. Tolson E (2001) Machine Learning in the area of image analysis and pattern recognition. Advanced Undergraduate Project, Spring
22. Loh W, Tim L, Shih YS (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn 40(3):203-228
23. Copkun N, Yildirim T (2003) Image segmentation using statistical neural networks. In: International conference on artificial neural networks/ international conference on neural information processing (ICANN/ICONIP), June 26-29, 2003, Istanbul, pp 154-156
24. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Annu Eugen 7(II):179-188
25. Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York, pp 218. ISBN 0-471-22361-1
26. Dasarathy BV (1980) Nosing around the neighborhood: a new system structure and classification rule for recognition in partially exposed environments. IEEE Trans Pattern Anal Mach Intell 2(1):67-71
27. Gates GW (1972) The reduced nearest neighbor rule. IEEE Trans Inf Theory May 1972, pp 431-433
28. Duchene J, Leclercq S (1988) An optimal transformation for discriminant and principal component analysis. IEEE Trans Pattern Anal Mach Intell 10(6):978-983
29. Domeniconi C, Gunopulos D, Peng J (2005) Large margin nearest neighbor classifiers. IEEE Trans Neural Netw 16(4):899-909