Large scale manifold transduction

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 448-455

  Karlen, Michael ^a,b   Weston, Jason ^a   Erkan, Ayse ^a,c   Collobert, Ronan ^a  

^a NEC LABORATORIES AMERICA   (United States)

^b EPFL   (Switzerland)

^c POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GRADIENT METHODS; MACHINE LEARNING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; SUPPORT VECTOR MACHINES; LEARNING SYSTEMS; ROBOT LEARNING;

LOSS FUNCTIONS; MULTI-LAYER ARCHITECTURES; NATURAL GENERALIZATION; PRIOR KNOWLEDGE; STATE OF THE ART; STOCHASTIC GRADIENT DESCENT; TRAINING AND TESTING; TRANSDUCTIVE SUPPORT VECTOR MACHINE;

NETWORK ARCHITECTURE; STOCHASTIC MODELS;

ERROR RATES; LINEAR MODELS; LOSS FUNCTIONS; MULTI LAYERS; NATURAL GENERALIZATIONS; PRIOR KNOWLEDGE; REGULARIZER; STOCHASTIC GRADIENT DESCENTS; TRAINING AND TESTING; TWO STAGES;

EID: 56449125402     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390213     Document Type: Conference Paper

References (21)

1. Belkin, M., & Niyogi, P. (2003). Laplacian Eigenmaps for Dimensionality Reduction and Data Representation.
2. Belkin, M., Niyogi, P., & Sindhwani, V. (2006). Manifold regularization: a geometric framework for learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research, 7, 2399-2434.
3. Bennet, K., & Demiriz, A. (1998). Semi-Supervised Support Vector Machines. NIPS 12, 1998. MIT Press, Cambridge, MA.
4. Bottou, L. (2007). http://leon.bottou.org/projects/sgd.
5. Burges, C. (1996). Simplified Support Vector Decision Rules. ICML, 71-77.
6. Chapelle, O., Schölkopf, B., & Zien, A. (2006). Semi-supervised learning. Adaptive computation and machine learning. Cambridge, Mass., USA: MIT Press.
7. Chapelle, O., Weston, J., & Schölkopf, B. (2003). Cluster kernels for semi-supervised learning. NIPS 15 (pp. 585-592). Cambridge, MA, USA: MIT Press.
8. Chapelle, O., & Zien, A. (2005). Semi-supervised classification by low density separation. AISTATS (pp. 57-64).
9. Collobert, R., Sinz, F., Weston, J., & Bottou, L. (2006). Large scale transductive svms. Journal of Machine Learning Research, 7, 1687-1712.
10. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis. New York: Wiley & Sons.
11. Fung, G., & Mangasarian, O. (2001). Semi-supervised support vector machines for unlabeled data classification. Optimization Methods and Software, 15, 29-44.
12. Joachims, T. (1999). Transductive inference for text classification using support vector machines. International Conference on Machine Learning, ICML.
13. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86.
14. Ng, A. Y., Jordan, M., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
15. Sindhwani, V., & Keerthi, S. S. (2006). Large scale semi-supervised linear SVMs. SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 477-484). New York, NY, USA: ACM Press.
16. Sindhwani, V., Niyogi, P., & Belkin, M. (2005). Beyond the point cloud: from transductive to semi-supervised learning. International Conference on Machine Learning, ICML.
17. Steinwart, I., & Scovel, C. (2005). Fast rates to bayes for kernel machines. NIPS, 17, 1345-1352.
18. Tenenbaum, J., de Silva, V., & Langford, J. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319-2323.
19. Vapnik, V. N. (1998). Statistical learning theory. John Wiley and Sons, New York.
20. Xu, L., Neufeld, J., Larson, B., & Schuurmans, D. (2005). Maximum margin clustering. Advances in Neural Information Processing Systems, 17, 1537-1544.
21. Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation (Technical Report CMU-CALD-02-107). Carnegie Mellon University.