Optimization approaches for semi-supervised multiclass classification

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2006, Pages 863-867

  Yajima, Yasutoshi ^a   Kuo, Tien Fang ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; LAPLACE TRANSFORMS; LEARNING ALGORITHMS; MACHINE LEARNING; MATRIX ALGEBRA; OPTIMIZATION; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

BINARY CLASSIFICATION METHODS; LAPLACIAN OF A GRAPH; LARGE-SCALE PROBLEM; MULTI-CLASS CLASSIFICATION; OPTIMIZATION APPROACH; OPTIMIZATION PROBLEMS; SEMI-SUPERVISED LEARNING METHODS; STANDARD OPTIMIZATION;

CLASSIFICATION (OF INFORMATION);

EID: 78449291474     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/icdmw.2006.128     Document Type: Conference Paper

References (6)

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3. A. Smola and I. Kondor. Kernels and regularization on graphs. In B. Schölkopf and M. Warmuth, editors, Proceedings of the Annual Conference on Computational Learning Theory, Lecture Notes in Computer Science. Springer, 2003.
4. D. Zhou, O. Bousquet, T. N. Lal, J.Weston, and B. Schölkopf. Learning with local and global consistency. Advances in Neural Information Processing Systems, 16:321-328, 2004.
5. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
6. X. Zhu, J. Lafferty, and Z. Ghahramani. Semi-supervised learning: From Gaussian fields to Gaussian processes. Technical Report CMU-CS-03-175, Carnegie Mellon University, 2003.