The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
The Evidence Framework Applied to Classification Networks
Abstract
Three Bayesian ideas are presented for supervised adaptive classifiers. First, it is argued that the output of a classifier should be obtained by marginalizing over the posterior distribution of the parameters; a simple approximation to this integral is proposed and demonstrated. This involves a "moderation" of the most probable classifier's outputs, and yields improved performance. Second, it is demonstrated that the Bayesian framework for model comparison described for regression models in MacKay (1992a,b) can also be applied to classification problems. This framework successfully chooses the magnitude of weight decay terms, and ranks solutions found using different numbers of hidden units. Third, an information-based data selection criterion is derived and demonstrated within this framework.
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Cites
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,388
Self-citationA practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
Self-citationBridle, J. S. (1990) · Neurocomputing · doi:10.1007/978-3-642-76153-9_28 · Cited by 1,237
Information-based objective functions for active data selection
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.4.590 · Cited by 1,221
Self-citationLearning and relearning in Boltzmann machines
Hinton, G. E., & Sejnowski, T. J. (2001) · Graphical Models · doi:10.7551/mitpress/3349.003.0005 · Cited by 1,037
Statistical mechanics of learning from examples
Seung, H. S., Sompolinsky, H., & Tishby, N. (1992) · Physical Review A · doi:10.1103/physreva.45.6056 · Cited by 532
Sequential updating of conditional probabilities on directed graphical structures
Spiegelhalter, D. J., & Lauritzen, S. L. (1990) · Networks · doi:10.1002/net.3230200507 · Cited by 521
Accelerated learning in layered neural networks
Solla, S. A., Levin, E., & Fleisher, M. (1988) · Complex Systems · Cited by 213
Learning algorithms and probability distributions in feed-forward and feed-back networks
Hopfield, J. J. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.23.8429 · Cited by 189
Neural net algorithms that learn in polynomial time from examples and queries
Baum, E. B. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.80287 · Cited by 168
Cited by
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
Self-citationInformation-based objective functions for active data selection
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.4.590 · Cited by 1,221
Self-citationA Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208
Benchmarking state-of-the-art classification algorithms for credit scoring
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003) · Journal of the Operational Research Society · doi:10.1057/palgrave.jors.2601545 · Cited by 904
Metadata
- Authors
- MacKay, D. J. C.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1992-09
- Volume
- 4
- Issue
- 5
- Pages
- 720-736
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2088538739
- Cited by
- 722
- References
- 13
- FWCI
- 8.94
- Type
- journal article
- Language
- en
- Open access
- closed
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