ManifoldBoost: Stagewise function approximation for fully-, semi-and un-supervised learning

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 600-607

  Loeff, Nicolas ^a   Forsyth, David ^a   Ramachandran, Deepak ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; PROBABILITY DENSITY FUNCTION; RADIAL BASIS FUNCTION NETWORKS; ROBOT LEARNING; SUPERVISED LEARNING; FUNCTIONS;

DATA SETS; FUNCTION APPROXIMATIONS; FUNCTIONAL MINIMIZATIONS; MANIFOLD LEARNINGS; PARTIALLY SUPERVISED LEARNINGS; RADIAL BASIS FUNCTIONS; SEMI-SUPERVISED LEARNINGS; STATE OF THE ARTS; UNSUPERVISED CLUSTERING;

EDUCATION;

FUNCTION APPROXIMATIONS; FUNCTIONAL MINIMIZATIONS; LARGE-SCALE DATASETS; MANIFOLD LEARNING; PARTIALLY SUPERVISED LEARNING; RADIAL BASIS FUNCTIONS; SEMI-SUPERVISED LEARNING; STATE-OF-THE ARTS; UNSUPERVISED CLUSTERING;

EID: 56449119811     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bertsekas, D. P. (1996). Constrained optimization and lagrange multiplier methods. Academic Press.
2. Bickel, P., & Li, B. (2007). Local polynomial regression on unknown manifolds. IMS Lecture Notes Monograph Series: Complex Datasets and Inverse Problems: Tomography, Networks and Beyond (pp. 177-186).
3. Chapelle, O., Schölkopf, B., & Zien, A. (2006). Semi-supervised Learning. MIT Press.
4. Chen, K., & Wang, S. (2008). Regularized boost for semi-supervised learning. NIPS 20 (pp. 281-288).
5. Donoho, D. L., & Grimes, C. (2003). Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. Proc. Natl. Acad. Sci. USA, 100, 5591-5596.
6. Dyn, N., Levin, D., & Rippa, S. (1986). Numerical procedures for surface fitting of scattered data by radial functions. SIAM Journal on Scientific and Statistical Computing, 7, 639-659.
7. Friedman, J. (1999). Greedy function approximation: a gradient boosting machine (Technical Report). Dept. of Statistics, Stanford University.
8. Joachims, T. (1999). Transductive inference for text classification using support vector machines. ICML (pp. 200-209).
9. Kégl, B., & Wang, L. (2005). Boosting on manifolds: Adaptive regularization of base classifiers. NIPS 17 (pp. 665-672).
10. Lafferty, J., & Wasserman, L. (2007). Statistical analysis of semi-supervised regression. NIPS 20 (pp. 801-808).
11. M. Belkin, I. M., & Niyogi, P. (2004). Regression and regularization on large graphs. COLT (pp. 824-831).
12. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (2000). Boosting algorithms as gradient descent. NIPS 12 (pp. 512-518).
13. Niyogi, P. (2008). Manifold regularization and semi-supervised learning: Some theoretical analyses (Technical Report). University of Chicago. Technical Report TR-2008-01, Computer Science Dept.
14. Roweis, S., & Saul, L. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
15. Sindhwani, V., Belkin, M., & Niyogi, P. (2006). The geometric basis of semi-supervised learning. In Chapelle, Schoelkopf and Zien (Eds.), Semi-supervised learning. MIT Press.
16. Tenenbaum, J. B., de Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319-2323.
17. Zhu, X. (2006). Semi-supervised learning literature review (Technical Report). University of Wisconsin.
18. Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using gaussian fields and harmonic functions. ICML (pp. 912-919).