Mixture of Gaussian Processes for combining multiple modalities

Lecture Notes in Computer Science

Volumn 3541, Issue , 2005, Pages 86-96

  Kapoor, Ashish ^a   Ahn, Hyungil ^a   Picard, Rosalind W ^a  

^a MIT MEDIA LAB   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA REDUCTION; PARAMETER ESTIMATION; PROBABILITY;

CLASSIFIER COMBINATION SCHEMES; EXPECTATION PROPAGATION; GAUSSIAN PROCESSES; MULTIPLE MODALITIES;

GAUSSIAN NOISE (ELECTRONIC);

EID: 26444540745     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11494683_9     Document Type: Conference Paper

References (15)

1. M. J. Beal, Variational Algorithms for Approximate Bayesian Inference, Ph.D. Thesis, University College London, 2003.
2. L. Breiman, Bagging Predictors, Machine Learning, Vol. 26(2), 1996.
3. J. Han, B. Bhanu, Statistical Feature Fusion for Gait-based Human Recognition, CVPR, 2004.
4. L. Hong, and A. K. Jain, Integrating Faces and Fingerprints for Personal Identification, PAMI, Vol. 20(1), 1998.
5. R. A. Jacobs, M. I. Jordan, S. J. Nowlan and G .E. Hinton, Adaptive Mixtures of Local Experts, Neural Computation, Vol. 3, pp. 79-87, 1991.
6. A. Kapoor, R. W. Picard and Y. Ivanov, Probabilistic Combination of Classifiers to Detect Interest, ICPR, 2004.
7. J. Kittler, M. Hatef, R. P. W. Duin and J. Matas, On Combining Classifiers, PAMI, Vol. 20(3), 1998.
8. D. J. Miller and L. Yan, Critic-Driven Ensemble Classification, Signal Processing, Vol. 47(10), 1999.
9. T. Minka, Expectation Propagation for Approximate Bayesian Inference, UAI, 2001.
10. N. Oliver, A. Garg and E. Horvitz, Layered Representations for Learning and Inferring Office Activity from Multiple Sensory Channels, ICMI, 2002.
11. M. Opper and O. Winther, Mean field methods for Classification with Gaussian Processes, NIPS, Vol. 11, 1999.
12. R. Schapire, A Brief Introduction to Boosting, International Conference on Algorithmic Learning Theory, 1999.
13. K. Toyama and E. Horvitz, Bayesian Modality Fusion: Probabilistic Integration of Multiple Vision Algorithms for Head Tracking, ACCV, 2000.
14. V. Tresp, Mixture of Gaussian Processes, NIPS, Vol. 13, 2001.
15. D. Zhou, O. Bousquet, T. N. Lal, J. Weston and B. Scholkopf, Learning with Local and Global Consistency, NIPS, Vol. 16, 2004.