Kernel space for text analysis based on fuzzy neighborhoods

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2008, Pages 738-743

  Miyamoto, Sadaaki ^a   Kawasaki, Yuichi ^a  

^a UNIVERSITY OF TSUKUBA   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY LOGIC; FUZZY SYSTEMS; MODAL ANALYSIS; STRUCTURAL OPTIMIZATION;

C-MEANS CLUSTERING; DOCUMENT MODELS; EUCLIDEAN SPACES; HIERARCHICAL STRUCTURES; KERNEL FUNCTIONS; KERNEL SPACES; METHODS OF DATA ANALYSES; NUMERICAL EXAMPLES; PRINCIPAL COMPONENTS; TEXT ANALYSES; TOPOLOGICAL STRUCTURES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 55249083552     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2008.4630452     Document Type: Conference Paper

References (17)

1. J.C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York. 1981.
2. D. Dubois, H. Prade, Rough fuzzy sets and fuzzy rough sets, Int. J. General Systems. 17, pp. 191-209, 1990.
3. B.S. Everitt, Cluster Analysis, 3rd Ed., Arnold, London, 1993.
4. D. Haussler, Convolution kernels on discrete structures, Technical Report UCSC-CRL-99-10. UC Santa Cruz, 1999.
5. S. Miyamoto, Fuzzy Sets in Information Retrieval and Cluster Analysis, Kluwer, Dordrecht, 1990.
6. S. Miyamoto, D. Suizu, Fuzzy c-means clustering using transformations into high dimensional spaces, Proc. of FSKD'02: 1st International Conference on Fuzzy Systems and Knowledge Discovery. Nov. 18-22, 2002, Singapore, Vol.2, pp. 656-660.
7. S. Miyamoto, Y. Nakayama, Algorithms of hard c-means clustering using kernel functions in support vector machines, Journal of Advanced Computational Intelligence and Intelligent Informatics. 7(1) pp. 19-24, 2003.
8. S. Miyamoto, D. Suizu, Fuzzy c-means clustering using kernel functions in support vector machines, Journal of Advanced Computational Intelligence and Intelligent Informatics. 7(1) pp. 25-30, 2003.
9. S. Miyamoto, Generalizations of multisets and rough approximations, International Journal of Intelligent Systems, 19(7) pp. 639-652, 2004.
10. S. Miyamoto, S. Hayakawa, A Fuzzy Neighborhood Model for Clustering, Classification, and Approximations. S. Greco et al. (Eds.): RSCTC 2006, LNAI 4259, pp.882-890, Springer-Verlag. Berlin. 2006.
11. S. Miyamoto, K. Mizutani, Fuzzy multiset space and c-means clustering using kernels with application to information retrieval, Proc. of IFSA2003, Istanbul, Turkey June 29- July 3, 2003, In T. Bilgic et al., eds., Lecture Note in Artificial Intelligence, LNAI 2715, pp. 387-395, 2003.
12. G. Pólya, Remarks on characteristic functions, Proc. First Berkeley Conf. on Math. Stat. and Prob., pp. 115-123, Univ. of Calf Press, Berkeley, 1949.
13. G. Salton, M.J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983.
14. I.J. Schoenberg, Metric spaces and positive definite functions. Trans. Amer. Math. Soc. 44 pp. 522-536, 1938.
15. B Schölkopf, A Smola, K.R. Müller, Kernel principal component analysis. Advances in Kernel Methods-Support Vector Learning, 1999, pp.1-6.
16. V.N. Vapnik, Statistical Learning Theory, Wiley, 1998.
17. G. Young, A.S. Householder, Discussion of a set of points in terms of their mutual distances, Psychometrika, Vol.3, No.1, pp. 19-22, 1938.