Distance-based selection of potential support vectors by kernel matrix

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3173, Issue , 2004, Pages 468-473

  Li, Baoqing ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

GEOMETRY; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; QUADRATIC PROGRAMMING; STATISTICS; VECTORS;

DISTANCE-BASED METHODS; GENERALIZATION ABILITY; INTERMEDIATE STAGE; K NEAREST NEIGHBOR ALGORITHM; KERNEL MATRICES; MEMORY CAPACITY; OPTIMAL HYPERPLANES; QUADRATIC PROGRAMMING PROBLEMS;

MATRIX ALGEBRA;

EID: 35048852062     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28647-9_78     Document Type: Article

References (5)

1. Vapnik, V.N.: The Nature of Statistical Learning Theory. 2nd edn. Springer (1999)
2. Vapnik, V.N.: Estimation of Dependencies Based on Empirical Data. Springer (1982)
3. Osuna, E.E., Freund, R., Girosi, F.: Support vector machines: Training and applications. A.I.Memo 1602, Massachusetts Institute of Technology (1997)
4. Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In Scholköpf, B., Burges, C.J.C., Smola, A.J., eds.: Advances in Kernel Methods: Support Vector Learning. The MIT Press (1998)
5. Scholköpf, B.: The kernel trick for distances. In Leen, T., Dietterich, T., Tresp, V., eds.: Advances in Neural Information Processing Systems. Vol. 13. The MIT Press (2000)