Gaining insights into support vector machine pattern classifiers using projection-based tour methods

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2001, Pages 251-256

  Caragea, Doina ^a   Cook, Dianne ^b   Honavar, Vasant ^a  

^a Iowa State University   (United States)

^b Iowa State University of Science and Technology   (United States)

Author keywords

Classification; Dynamic graphics; Machine leaning; Multivariate data; Support vector machines; Tours; Visualization

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL TRANSFORMATIONS; PATTERN RECOGNITION; VISUALIZATION;

SUPPORT VECTOR MACHINES;

DATA MINING;

EID: 0035789668     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502512.502547     Document Type: Conference Paper

References (17)

1. D. Asimov. The Grand Tour: A Tool for Viewing Multidimensional Data. SIAM Journal of Scientific and Statistical Computing, 6(1):128-143, 1985.
2. M. Brown, W. Grundy, D. Lin, N. Christianini, C. Sugnet, T. Furey, M. Ares Jr., and D. Haussler. Knowledge based analysis of microarray gene expression data using support vector machines. TR UCSC CRL-99-09, CRL, Santa Cruz, CA., 1999.
3. A. Buja, D. Asimov, C. Hurley, and J. A. McDonald. Elements of a Viewing Pipeline for Data Analysis. In W. S. Cleveland and M. E. McGill editors, Dynamic Graphics for Statistics, pages 277-308. Wadsworth, Monterey, CA, 1988.
4. A. Buja, D. Cook, D. Asimov, and C. Hurley. Dynamic Projections in High-Dimensional Visualization: Theory and Computational Methods. TR, AT&T Labs, Florham Park, NJ, 1997.
5. D. Caragea, A. Silvescu, and V. Honavar. Agents that learn from distributed dynamic data sources. In Proc. of the Workshop on Learning Agents, Agents 2000/ECML 2000, Barcelona, Spain, 53-61, 2000.
6. D. Caragea, A. Silvescu, and V. Honavar. Analysis and synthesis of agents that learn from distributed dynamic data sources. In S. E. A. Wermter, Ed., Neural Network Architectures Inspired by Neuroscience. Springer-Verlag, 2001.
7. D. Cook and A. Buja. Manual Controls For High-Dimensional Data Projections. Journal of Computational and Graphical Statistics, 6(4):464-480, 1997. Also see www.public.iastate.edu/̃dicook/research/papers/manip.html.
8. D. Cook, A. Buja, J. Cabrera, and C. Hurley. Grand Tour and Projection Pursuit. Journal of Comp. and Graphical Statistics, 4(3):155-172, 1995.
9. C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:273-297, 1995.
10. D. J. Donnell, A. Buja, and W. Stuetzle. Analysis of Additive Dependencies using Smallest Additive Principle Components. Annals of Statistics, 22, 1994.
11. M. Forina, C. Armanino, S. Lanteri, and E. Tiscornia. Classification of olive oils from their fatty acid composition. In H. Martens and H. Russwurm Jr., editors, Food Research and Data Analysis, pages 189-214. Applied Science Publishers, London, 1983.
12. G. Furnas and A. Buja. Prosection Views: Dimensional Inference Through Sections and Projections. Journal of Computational and Graphical Statistics, 3(4):323-385, 1994.
13. M. Hearst, B. Scholkopf, S. Dumais, E. Osuna, and J. Platt. Support vector machines. IEEE Intelligent Systems, 13(4):18-28, 1998.
14. T. Joachims. Making Large-Scale SVM Learning Practical. MIT Press, 1999.
15. V. Vapnik. The Nature of Statistical Learning Theory (Statistics for Engineering and Information Science). Springer-Verlag, New York, NY, 1999.
16. www.public.iastate.edu/̃dicook/JSS/paper
17. Limn: www.public.iastate.edu/dicook/Limn