Analysis of spectral kernel design based semi-supervised learning

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 1601-1608

  Zhang, Tong ^a   Ando, Rie Kubota ^b  

^a YAHOO INC   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA GRAPH; GENERALIZATION PERFORMANCE; KERNEL DESIGN; PREDICTIVE PERFORMANCE; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED LEARNING METHODS; SPECTRAL DECOMPOSITION; SPECTRAL KERNELS;

DESIGN;

SUPERVISED LEARNING;

EID: 84864072823     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Mikhail Belkin and Partha Niyogi. Semi-supervised learning on Riemannian manifolds. Machine Learning, Special Issue on Clustering:209-239, 2004.
2. Olivier Chapelle, Jason Weston, and Bernhard Sch:olkopf. Cluster kernels for semisupervised learning. In NIPS, 2003.
3. Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In NIPS, pages 849-856, 2001.
4. M. Szummer and T. Jaakkola. Partially labeled classification with Markov random walks. In NIPS 2001, 2002.
5. D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schlkopf. Learning with local and global consistency. In NIPS 2003, pages 321-328, 2004.
6. Xiaojin Zhu, Zoubin Ghahramani, and John Lafferty. Semi-supervised learning using Gaussian fields and harmonic functions. In ICML 2003, 2003.