Bayesian hierarchical ordinal regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3697 LNCS, Issue , 2005, Pages 267-272

  Paquet, Ulrich ^a   Holden, Sean ^a   Naish Guzman, Andrew ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BENCHMARKING; DATA STRUCTURES; NEURAL NETWORKS;

BAYESIAN APPROACH; INPUT SPACE; ORDINAL REGRESSION; SOFT PARTITIONING;

REGRESSION ANALYSIS;

EID: 33646259490     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (5)

1. Chu, W. & Ghahramani, Z. (2004) Gaussian processes for ordinal regression. Technical report, Gatsby Computational Neuroscience Unit, University College London.
2. Jordan, M. I. & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.
3. MacKay, D. J. C. (1992). The evidence framework applied to classification networks. Neural Computation, 4(5), 698-714.
4. Shashua, A. & Levin, A. (2003). Ranking with large margin principle: two approaches. In Advances in Neural Information Processing Systems 15, (pp. 937-944). MIT Press
5. Waterhouse, S. R., MacKay, D. J. C., & Robinson, A. J. (1996). Bayesian methods for mixtures of experts. In Advances in Neural Information Processing Systems 8, (pp. 351-357). MIT Press.