Optimising newspaper sales using neural-Bayesian technology

Neural Computing and Applications

Volumn 12, Issue 3-4, 2003, Pages 212-219

  Heskes, Tom ^a   Spanjers, Jan Joost ^a   Bakker, Bart ^a   Wiegerinck, Wim ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

Bayesian inference; Feed forward; Multi task learning; Neural networks; Time series prediction

Indexed keywords

APPROXIMATION THEORY; DECISION MAKING; HIERARCHICAL SYSTEMS; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MULTITASKING; NEWSPRINT; OPTIMIZATION; PROBABILISTIC LOGICS; SALES; TIME SERIES ANALYSIS;

BAYESIAN ANALYSIS; NEWSPAPERS;

FEEDFORWARD NEURAL NETWORKS;

EID: 0346342558     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-003-0384-x     Document Type: Article

References (14)

1. Ragg T, Menzel W, Baum W and Wigbers M (2002) Bayesian learning for sales rate prediction for thousands of retailers. Neurocomputing 43:127-144
2. MacKay D (1995) Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Network 6:469-505
3. Heskes T (1998) Solving a huge number of similar tasks: a combination of multi-task learning and a hierarchical Bayesian approach. In: Proceedings of the International Conference on Machine Learning, Morgan Kaufmann, San Mateo, CA
4. Heskes T (2000) Empirical Bayes for learning to learn. In: Langley P (ed) Proceedings of the Seventeenth International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA
5. Bakker B, Heskes T (2003) Task clustering and gating for Bayesian multitask learning. J Mach Learn Res 4:83-99
6. Caruana R (1997) Multitask learning. Mach Learn 28:41-75
7. Baxter J (1997) A Bayesian/information theoretic model of learning to learn via multiple task sampling. Mach Learn 28:7-39
8. Cadez I, Ganey S and Smyth P (2000) A general probabilistic framework for clustering individuals. Technical report, University of California, Irvine, CA
9. Bryk A, Raudenbusch S (1992) Hierarchical linear models. Sage, Newbury Park, UK
10. Robert C (1994) The Bayesian choice: a decision-theoretic motivation. Springer, Berlin Heidelberg New York
11. Wolpert D (1993) On the use of evidence in neural networks. In: Hanson S, Cowan J and Giles L (eds) Advances in neural information processing systems 5, Morgan Kaufmann, San Mateo, CA
12. Dempster A, Laird N and Rubin D (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B 39:1-38
13. West M, Harrison J (eds) (1977) Bayesian forecasting and dynamic models. Springer, Berlin Heidelberg New York
14. Gamerman D, Migon H (1993) Dynamic hierarchical models. J Roy Stat Soc B 55:629-642